- Email: [email protected]
Rethinking the role of occupant behavior in building energy performance: A review
Accepted Manuscript
Rethinking the role of occupant behavior in building energy performance: A review Yan Zhang , Xuemei Bai , Franklin P. Mills , John C.V. Pezzey PII: DOI: Reference:
S0378-7788(18)30757-6 10.1016/j.enbuild.2018.05.017 ENB 8558
To appear in:
Energy & Buildings
Received date: Revised date: Accepted date:
7 March 2018 2 May 2018 7 May 2018
Please cite this article as: Yan Zhang , Xuemei Bai , Franklin P. Mills , John C.V. Pezzey , Rethinking the role of occupant behavior in building energy performance: A review, Energy & Buildings (2018), doi: 10.1016/j.enbuild.2018.05.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Highlights: A basic and comprehensive picture of research on occupant behavior and building energy performance is presented by systematic review of the literature.
Four critical research topics are identified. Energy-saving potential of occupant behavior is discussed and estimated to be in the range of 10%-25% for residential buildings and 5%-30% for commercial buildings.
Four existing research gaps are identified and discussed.
AC
CE
PT
ED
M
AN US
CR IP T
1
ACCEPTED MANUSCRIPT
Rethinking the role of occupant behavior in building energy performance: A review Yan Zhang a, Xuemei Bai a, *, Franklin P. Mills a, John C. V. Pezzey a
* Corresponding authors: Prof. Xuemei Bai Tel: +61 2 6125 7825
AN US
Email address:
CR IP T
a. Fenner School of Environment & Society, Australian National University, Linnaeus Way, Canberra, ACT 2601, Australia
[email protected] Postal address:
M
Frank Fenner Building #141, Linnaeus Way, ANU, Canberra ACT 2601,
AC
CE
PT
ED
Australia
2
ACCEPTED MANUSCRIPT
Abstract:
ED
Declaration of interest:
M
AN US
CR IP T
Technology alone will not achieve building energy conservation goals, and humans and their energy-related behavior in buildings must be included in energy performance efforts. Despite many studies revolving around human behavior and building energy performance, the understanding of occupant behavior and its role in building energy performance remains vague, confusing and inconsistent. We attempt to rethink occupant behavior and its role in building energy performance by means of review. Relevant articles have been collected from Web of Science and the basic picture of research has been presented. In-depth review focuses on four critical research topics: a) the current understanding of occupant behavior, with particular focus on window opening behavior, lighting control behavior, and space heating/cooling behavior; b) methods and techniques for collecting data on behavior and building energy performance; c) quantitative modeling of occupant behavior and building energy performance; and d) evaluation of energy saving potentials of occupant behavior based on existing literature. We estimate the energy-saving potential of occupant behavior to be in the range of 10%-25% for residential buildings and 5%-30% for commercial buildings, based on findings of existing research. From our analyses, we identify four existing research gaps, namely the needs for understanding occupant behavior in a systematic framework; for stronger empirical evidence beyond individual buildings and at a larger city scale; for linking occupant behavior to socio-economic and policy variables; and for evaluating the role of occupant behavior in the effectiveness of building energy efficiency policy.
PT
This work was partly funded by the China Scholarship Council (CSC) under the Grant No. 201506030015.
AC
CE
Keywords: Occupant behavior; Buildings; Building energy performance; Building energy efficiency
3
ACCEPTED MANUSCRIPT
1. Introduction
AN US
CR IP T
Energy and climate issues have gained extensive attention of both the public and academia as human and natural systems are being exposed to more risks caused by ever-increasing greenhouse gas (GHG) emissions from primary energy consumption [1, 2], including increasing severity and frequency of extreme weather across the globe such as heat and cold waves, flash floods and tropical cyclones [3, 4]. The building sector accounted for nearly 40% of total global energy use and 33% of total GHG emissions in 2016 [5], which would be even greater if the embodied energy use and related emissions are taken into account [6-8] for the whole life cycle of buildings. Energy consumption from buildings and activities in buildings is projected to increase by an average of 1.5% per year for the period of 2012-2040 under a business as usual scenario [9], and may double or even triple by 2050 from the level of 2010 [10]. Although building energy demand in developing countries and global building energy use have been trending upward [10], the massive, cost-effective opportunities for saving energy in the building sector could largely curb or even reverse growth in global energy demand within and beyond buildings [8, 11-13]. Thus, the building energy sector represents a significant opportunity for both accelerating the energy transition and ensuring a low-carbon future worldwide [10, 14, 15].
AC
CE
PT
ED
M
Technological innovations and improvements have long been at the center of policies and approaches to improving building energy performance and reducing building energy use. For example, the International Energy Agency [16] projected great energy savings (approximately 6 exajoules in total) could be achieved from building envelopes in 2050 under the 2ºC maximum warming scenario from a wide range of building envelope technologies, such as advanced facades, highly insulated windows, automated dynamic shading and glazing, and energy-efficient building materials. Moreover, a report [17] for the U.S. Department of Energy developed a research and development roadmap for heating, ventilation, and air conditioning (HVAC) technologies, identifying high-priority initiatives for high efficiency HVAC technologies like advanced direct-current-powered HVAC system, techniques improving heat pump performance at low-ambient temperatures, and electrochemical compression systems. Other building energy efficiency technologies, such as energy efficient appliances [18] and building automation and control systems [19, 20], have also been widely discussed. However, neither significant improvement of per capita final energy use from buildings (Figure 1) nor the expected energy use reductions [21] has been seen as a result of these technological solutions and efforts. This is partly due to low adoption rates which have been limited to some extent by the high cost of adopting new energy efficiency technologies [22, 23]. Moreover, some recent surveys in the UK [21] and Finland [24] have revealed that over 40% of the population are not interested in new technologies and are thus reluctant to purchase and adopt an energy-efficient technology or service. Researchers also have found that even for almost identical buildings, there can be huge disparities among their annual energy consumptions [25-27]. Furthermore, there are emerging novel 4
ACCEPTED MANUSCRIPT
research agendas that aim to investigate and highlight occupant behavior as a critical influence on building energy consumption that can maximize building energy efficiency to the same extent that technological solutions can [28-30].
AC
CE
PT
ED
M
AN US
CR IP T
More and more researchers [31-36] have come to realize the importance of occupant behavior in building energy performance, identifying occupant behavior as the main cause of the ineffectiveness of technology solutions and the unrealized savings of energy consumption in buildings. Better understanding of human activities, urban energy system and climate responses in cities, as well as their interactions, is urgently needed to reduce carbon emissions and urban risks from global warming [37, 38]. Additionally, for developing countries, the building energy consumption is likely to increase with the rapid urbanization and economic development, during which occupants‘ awareness and behavior are critical to curbing the growth of energy use in buildings [39, 40]. Compared to technological approaches, behavioral programs and interventions can be highly cost-effective [41] and are more likely to be adopted by a wider range of building users. In terms of the implementation of building energy policies, some policymakers and officials in the building sector also have come to realize the ineluctable role of occupant behavior in the effectiveness of relevant strategies [22, 42]. To date, the studies looking at occupant behavior and building energy performance mostly investigate into one or two specific kinds of energy-related behavior in one building or area. In addition, although researchers from different disciplines have delved into occupant behavior and energy use in buildings, often without common language, the findings usually lack comparability and are difficult to generalize beyond the specific context for each study [43]. Thus, it is difficult to grasp a holistic understanding of occupant behavior and its impact on building energy performance. Specifically, these questions remain unanswered: a) what is the state-of-the-art understanding of occupant behavior in relation to energy use in buildings? b) how has the research evolved and what are the hot topics and research gaps? c) what kind of methodologies have been used for research? d) what is the range of energy saving potential of occupant behavior? This paper explores these questions through a systematic review of literature, as shown in Figure 2. The following section presents the research landscape of occupant behavior and building energy performance, followed by a bibliometric analysis of literature in Section 3, aiming at identifying research trends and key topics. Sections 4-7 explore the key foci for recent literature, as revealed by the bibliometric analysis. Section 8 discusses major research gaps, and Section 9 presents the conclusion.
5
CR IP T
ACCEPTED MANUSCRIPT
Figure 1. Annual per capita final energy use of residential and commercial buildings for eleven regions in
Figure 2 Research framework
CE
PT
ED
M
AN US
1990 and 2010 （Source: IPCC, 2014）
AC
2. Research landscape of occupant behavior and building energy performance Research on occupant behavior and building energy performance can be divided into five clusters, based on research purpose. One is to understand the environmental psychology of occupants by identifying and assessing the key factors that influence the energy-related behavior of building occupants [44-47]. In this context, occupant behavior is viewed as a bridge or link between key influential factors and typical energy consumption patterns. This research cluster identifies and studies the factors that have the greatest influence on behavior across a range of scenarios. The second cluster is about developing and testing intervention strategies and methods to reduce energy use. Information intervention appears to be an effective 6
ACCEPTED MANUSCRIPT
strategy that has been widely studied and applied across the world [48-53]. For example, Magali et al. (2013) conducted a meta-analysis of information-based energy conservation experiments, analyzing 56 published trials and 524,479 study subjects from 1975 to 2012 [54]. They found that individuals in the experiments reduced electricity consumption averagely by 7.4% and that individualized feedback via audits and consulting was the most effective information strategy.
M
AN US
CR IP T
The third cluster is about characterizing occupant attitudes and assessing the effectiveness of energy policies. A key finding is the need to integrate occupant behavior into the current policy framework for building energy efficiency. Although installation of more energy efficient equipment is a common policy, increasing occupants‘ awareness of energy-related behavior and thereby change their behavior is more cost-effective [22, 33]. Some research [55, 56] has highlighted the importance of occupant behavior in building energy performance through surveys that identify the energy saving potential from occupant behavior for the building sector. For instance, Hu et al. (2017) [57] conducted an online survey on energy consumption and behavior in urban Chinese households. They collected data on occupants and buildings, energy consuming devices and occupant usage of them, overall energy consumption, and occupants‘ satisfaction with the indoor environment, and occupants‘ attitudes toward energy-saving policies. Based on their analysis of relevant survey data, they concluded that China‘s urban households‘ behavior is decentralized, varying and with low energy consumption.
CE
PT
ED
The fourth cluster focuses on evaluating the effectiveness of energy efficient technology and building design [58-63]. This cluster of research uses occupant behavior models as an input when predicting the expected energy performance of building designs [64-67]. Mejri et al. (2011) [67] proposed a method to assess energy performance of occupied buildings using model identification techniques in order to improve the prediction of building energy performance. Ioannou and Itard (2015) [68] investigated energy simulation tools and analyzed building and behavioral parameters influencing the simulation results of residential heating energy.
AC
The last cluster has been developing predictive, quantitative models of occupant energy-related behavior. The significant gaps that have been identified between expected and actual energy consumption in many experiments [69-77] suggest significant improvements are needed to understand the impact of occupant behavior on energy use. 3. Bibliometric analysis: Research trends and hot topics Following the research landscape presented above, this section aims to identify research trends and hot topics by means of bibiometric analysis. Bibliometrics is a powerful quantitative tool to explore knowledge networks based on published literature and has been widely used for studying the structure and development of a research field [78, 79], including research of energy and climate change [80, 81]. 7
ACCEPTED MANUSCRIPT
CR IP T
This paper therefore applied bibliometrics to research of occupant behavior in buildings. A broad search strategy for academic literature published in English was adopted, using the online database Web of Science TM Core Collection – Citation Indexes (SCI-EXPANDED & SSCI) with the document type limited to research articles and reviews. The terms used for searching by topic were ―occupant* or human or user*‖ and ―behavio*‖ and ―energy‖ and ―building* or hous* or office*‖. The search was first carried out in October 2016 and updated twice in April and August 2017. There were 1321 entries obtained in total. Then 206 were excluded based on relevance as they are not about occupant behavior and building energy performance, and as a result, a collection of 1115 publications were reviewed in this study.
AC
CE
PT
ED
M
AN US
In general, there is a growing trend in the yearly output of articles from 1978 to 2016 (Figure 3), which can be divided into two phases. The first is during 1978~2004, when there were fewer than 10 papers published each year without significant growth in output. The first paper appeared in 1978, authored by Socolow [82] in Princeton. It examined the role of residents and their behavior in energy consumption for space heating, based on a five-year field study of identical townhouses in Twin Rivers, New Jersey. The results of the experiments in the Twin Rivers program confirmed that the residents and their energy-related behavior in houses mattered with observed variation in energy consumption of identical houses with different occupants [82]. In addition to Socolow‘s research, Seligman [83] and Sonderegger et al. [84] also highlighted the impact of occupant behavior on energy performance of residential buildings. Both of their studies were sourced from the Twin Rivers program, as used by Socolow [82]. After carrying out two attitudinal surveys from the perspective of social psychology, Seligman [83] concluded that the resident can play an important role in energy conservation that complements engineering solutions. Sonderegger et al. [84] found that about 33% of the variation in gas consumption of 205 identical townhouses could be caused by occupant-related consumption patterns, of which persistent occupant-related patterns (‗lifestyle‘) explained 18% of the variation and non-persistent patterns (‗change‘) 15% of it. Furthermore, some researchers turned their eyes to occupant behavior in commercial buildings. For instance, Norford et al. [72] made an effort to identify the reasons for the wide discrepancy between actual and predicted energy use in a ‗low-energy‘ office building, indicating what really mattered was how the building was used by its occupants, rather than how the building was constructed. The second phase is from 2005 onwards, with output of papers growing rapidly at around 20% per year, and now exceeding 200 publications in 2016. Recent growth may be partly because the role of occupant behavior in building energy performance and energy conservation has been highlighted by some influential reports like the IPCC Fifth Assessment Report [10] and the IIASA Global Energy Assessment [39]. Despite the incomplete retrieval of relevant publications in 2017, it is worth noting that a four-year project, IEA-EBC Annex 66 [85], just ended in 2017, centered on the definition and simulation of occupant behavior in buildings and produced over 100 journal 8
ACCEPTED MANUSCRIPT
AN US
CR IP T
publications during its term.
AC
CE
PT
ED
M
Figure 3 Publications from 1978 to 2016
9
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Figure 4 shows the distribution of publications by country of the author (not limited to the first author of a publication, including all authors), source journal, institution and author. In terms of the publications by country, the USA has a total of 238 papers in this research field, accounting for about 21.3% of the whole related literature, followed by the UK with 228 publications (20.4%). The average citations within the Web of Science of articles from the USA and the UK are 68.27 and 30.44, respectively. It is obvious that the USA occupies a leading position in research of occupant behavior and building energy performance, as shown in Figure 4(a). When it comes to the journals in which the papers were published, most of the studies are concentrated in journals whose 2016 Impact Factors ranging from 3 to 6, among which the journal Energy and Buildings (IF: 4.067) takes the first place (294, 26.4%)
10
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
by the number of articles. The institution with the largest number of publications is Tsinghua University (34, 3%), followed by Delft University of Technology (27, 2.4%) in Netherlands, Polytechnic University of Turin (27, 2.4%) in Italy and University of California, Berkeley (27, 2.4%) in the USA. Moreover, there are about 2875 authors involved in this field of research, and the number of authors who published at least 5 papers in our reviewed literature is 60 (2.1%). Of those 2875 authors, 2419 (84.1%) only have one publication and 290 (10.1%) have two publications, indicating that a limited group of researchers (166, 5.8%) have been focusing on this field. The top 12 authors having published at least 10 papers are as shown in Figure 4(b). The top two researchers, Hong TZ and Yan D, appears to be closely collaborating.
Figure 4 Distribution of publications
11
ACCEPTED MANUSCRIPT
CR IP T
Among those articles, there are 81 papers having been cited at least 81 times (i.e. h-index = 81). One of the most highly cited articles was published in Energy and Buildings in 2009, with a local citation score of 93 (i.e. 93 times being cited by the papers in our collection of literature) and 183 citations within the Web of Science. This article discussed the effect of user behavior on building performance from the perspective of the building design phase to perfect the building performance simulation, suggesting that more details should be integrated into the user behavior assessment for specific buildings and the actual occupants and their peculiarities need to be considered during the building design phase for better outcomes of building energy simulation [86]. In fact, recently a large body of research (236 papers, 21.2%) has focused on occupant behavior and its effect on building energy simulation [87-90], while some studies (78 papers, 7.0%) concentrated on those factors influencing occupant behavior [44, 46, 91, 92].
CE
ED
Understanding of occupant behavior, especially specific behaviors affecting indoor thermal comfort, such as HVAC-related behavior, and lighting- and window-related behavior (relevant keywords: thermal comfort, overheating, natural ventilation, lighting, ventilation, window opening, etc.) (Section 4) ; Research methods and energy data collection (relevant keywords: bottom-up approach, framework, monitoring, energy management, thermostats, etc.) (Section 5) ; Quantitative modeling of occupant behavior and building energy performance (relevant keywords: simulation, building energy simulation, model, optimization, energy modeling, etc.) (Section 6) ; Energy saving potential and behavioral strategies (relevant keywords: energy saving, feedback, information, intervention, etc.) (Section 7).
PT
M
AN US
By going through the keywords of the literature, hot topics can be identified with clustering techniques, as shown in Figure 5. The threshold for a keyword included in analysis is a frequency of at least 10. Each circle with a label represents a keyword and the size of the circle is determined by the frequency of the corresponding keyword. Lines between circles represent the links of keywords. The closer two keywords are located to each other, the more frequently they co-occur. Different colors refer to different clusters of keywords, and each cluster represents a hot topic of research. Therefore, four critical research topics can be identified as follows:
AC
Moreover, Figure 6 displays the research trends of occupant behavior in association with building energy performance, by means of presenting changes and evolution of hot topics over time from 2011 to 2016. All keywords shown in the figure appeared at least 10 times in the collection of literature. An average value of the published years of all papers on one topic is assigned to that topic, and used to determine the color in the figure. Collectively, the shifting color among the hot topics can reveal, to some extent, the shifting research focus. It can be seen that the research hot topics 12
ACCEPTED MANUSCRIPT
ED
M
AN US
CR IP T
shifted from behavioral strategies and psychological analysis to behavioral modeling and building energy simulation, which suggests a shift of research focus from qualitative research to quantitative analysis and modeling. Some specific research topics like indoor thermal comfort-related behavior and feedback strategies have obtained sustained research attention throughout the period. The hot topics identified above will be reviewed and discussed in depth in the following four sections.
AC
CE
PT
Figure 5 Clustering and network visualization of keywords
13
AN US
CR IP T
ACCEPTED MANUSCRIPT
M
Figure 6 Evolution of hot topics over time from 2011 to 2016
4. Understanding of energy-related occupant behavior
AC
CE
PT
ED
A lack of understanding of energy-related occupant behavior (hereafter, occupant behavior) in buildings has long been one significant hindrance to improving building energy performance [93]. Despite the fact that a large number of studies have investigated occupant behavior in the context of building energy, there is no consistent standard definition for the term ‗occupant behavior‘, with different researchers having different interpretations of defining and categorizing occupant behavior. Some studies included investment behavior in energy efficient measures of buildings (e.g. whether to buy an energy efficient appliance; whether to purchase and install solar panels for housing) as part of occupant behavior [22, 48, 94], while some strictly targeted energy-consuming activities when people occupied the buildings [69, 95-97], and some included non-energy-use behavior like putting on clothes and having hot drinks [98]. The International Energy Agency (IEA) in one of their reports attempted to define energy-related occupant behavior as ―observable actions or reactions of a person in response to external or internal stimuli, or respectively actions or reactions of a person to adapt to ambient environmental conditions such as temperatures, indoor air quality and sunlight‖ [99]. One project supported by the IEA Energy in Buildings and Community (EBC) Program for the period of 2013~2017 aimed to establish a standard occupant behavior definition platform to facilitate the quantitative simulation in buildings [85]. 14
ACCEPTED MANUSCRIPT
M
AN US
CR IP T
Considering the complex and stochastic characteristics of occupant behavior, Chen et al. (2015) developed a three level system of definitions of occupant behavior for different research purposes in residential buildings: 1) a simple-level definition with several parameters regarding the schedule of occupancy and domestic appliances for statistical analysis, 2) an intermediate-level definition with additional parameters about the set points of appliances and the operation schedule for case studies, and 3) a complex-level definition with descriptions on the schedule, set points and control rules of three types of occupant behavior, i.e. occupancy, appliance operation, and window/shading operation, for detailed diagnostics/simulations of building energy performance [93]. Gardner and Stern (1996) divided behaviors related to household energy conservation into two categories: efficiency and curtailment behaviors [100]. Efficiency behaviors are one-shot behaviors and involve the purchase of energy-efficient equipment, such as installation of thermal insulation. Curtailment behaviors involve repetitive efforts to reduce energy use, such as lowering thermostat settings. Wood and Newborough (2003) indicated that residential occupant behavior that can be improved covered both simple changes of energy use behavior and more complex, skill-oriented behavior related to interacting with household equipment [101]. Simple changes may include opening/closing a window or door, adjusting blinds, switching on/off lights, changing clothing, etc. More complex, skill-oriented behavior may require that the occupants have a good knowledge of the operation of energy-consuming equipment and how it works in households, like washing machines and stoves that involve complex decisions regarding how to set controls [102].
AC
CE
PT
ED
A substantial portion of the literature examined the occupant behavior in buildings with a particular focus on one or two specific types of behavior. Window opening behavior is one kind of energy-related behavior that has been widely discussed in studies. For example, Cali et al. (2016) analyzed window opening behavior in German households [103], and Jian et al. (2011) similarly focused on window opening behavior in five representative apartments in Beijing, China [104]. In particular, one highly cited paper proposed a theoretical framework to address interactions between occupants and building controls, highlighting the window opening behavior in buildings [46]. Fabi et al. [46, 105, 106] explained that the reason for window opening behavior obtaining so much research attention was that the window opening behavior has a very big impact both on the indoor environment quality and on the energy consumed to sustain the desired indoor environmental quality level. Some studies focused on the dominant factors and driving forces of window adjusting behavior of occupants in buildings [45, 95, 107], and some targeted effects of window opening behavior on energy consumption in buildings [108, 109]. For example, Wang and Greenberg [110] assessed various control strategies on window opening behaviors using the EnergyPlus simulation software. They pointed out the significant role of window opening behavior in occupants‘ indoor comfort and the HVAC system could achieve energy savings of up to 47% with mixed-mode ventilation for summers. Table 1 summarizes five categories of 15
ACCEPTED MANUSCRIPT
key variables influencing window opening behavior for residential and office buildings from the literature, including physiological factors (e.g. age and gender), psychological factors (e.g. perceived illumination and temperature preference), social factors (e.g. smoking behavior and presence at home), contextual factors (e.g. dwelling type, room type and season) and natural environmental factors (e.g. outdoor temperature, outdoor air quality and indoor temperature).
AN US
CR IP T
Apart from the window opening behavior, lighting control behavior also obtained exclusive attention from some researchers [111, 112]. For instance, Bourgeois et al. (2006) investigated the total energy effect of manual and automated lighting control based on a sub-hourly occupancy-based control model [90]. Ochoa et al. (2012) presented the current state of the art in lighting simulation, pointing out the lack of advanced models for representation of certain physical phenomena [113]. In particular, lighting-related behavior and its associated energy use in office buildings have been extensively studied with experiments by many researchers [53, 114-118]. Studies show that outdoor illuminance and occupant behavior are identified as two main factors influencing lighting energy use in buildings [114, 115, 119-121], and lighting behavior of occupants is in turn influenced by occupancy, time of the day and occupants movement within the building [116].
AC
CE
PT
ED
M
Another type of behavior that has been frequently discussed is space heating/cooling behavior. This is largely because the energy use associated with space heating and cooling significantly contributes to the total final energy use of buildings, both residential and commercial (up to 73% in some cases [122], on average 34% and 40% in residential and commercial buildings, respectively [10]). Experimental evidence has corroborated that heating energy use in residential buildings is significantly influenced by heating triggering/set-point temperature [123], which greatly depends on occupant behavior. Majcen et al. (2015) developed a statistical model for the heating prediction gap (i.e. the discrepancies between the normalized theoretical and actual heating consumption) with the example of households in the Netherlands, finding that significant factors affecting heating behavior and associated energy consumption involve occupants‘ perception of heat/cold, occupants‘ perception of dry/humid air, occupancy, time of the day, thermostat setting, ventilation system, and heating type [70].
16
ACCEPTED MANUSCRIPT
Table 1 Factors influencing window opening behavior in buildings (Source: summarized based on references [46] , [45] & [103])
Physiological Factors
Psychological Factors
Social Factors
Contextual Factors
Natural Environmental Factors
Age
Gender
Smoking behavior
Perceived illumination
Preference in terms of temperature
Presence at home
Room orientation
Outdoor quality
Ventilation type
Indoor temperature
Heating system
Indoor relative humidity
Solar radiation
Season
Wind speed
Time of day
CO2 concentrations
Window type
Outdoor temperature
Season
Time of day
Outdoor quality
Night ventilation
Indoor temperature
Indoor relative humidity
Solar radiation
Wind speed
Rain
AN US
Gender
Shared offices
Preference in terms of temperature
AC
CE
PT
Office buildings
Perceived illumination
ED
Age
M
Outdoor temperature
Room type
Residential buildings
Dwelling type
CR IP T
Building Type
5. Research methods and data collection techniques Occupant behavior is complex and stochastic in nature and it is hard to accurately describe or predict in the context of building energy performance. A rather common technical framework of research 17
air
air
AN US
CR IP T
ACCEPTED MANUSCRIPT
(
AC
CE
PT
ED
M
Figure 7) can be identified, which is used in many studies on occupant behavior and building energy performance. The initial step of research is data collection of occupant behavior and other relevant variables, together with modelling of occupant behavior. The choice of data collection approaches should align with the determination on behavioral models. Then the collected data and the chosen behavioral model will be used to analyze occupant behavior and building energy performance. As a result of the analysis, output of the relationship between occupant behavior and building energy performance can be determined. In order to validate the accuracy of developed models, an assessment of the models will be conducted based on the output. Finally, insights and implications of the research results can be explored. Clearly, it is essential to accurately select, capture, and model occupant behavior before further research on occupant behavior and building energy performance. Our analysis of the literature [124] shows data on occupant behavior are generally acquired through two main approaches – one is monitoring equipment installed within buildings (i.e. monitoring methods), and the other is depending on reports from occupants themselves (i.e. self-reports). Typical monitoring methods include ultrasonic and passive infrared detection, CO2 sensors, camera-based technologies, and wireless network based systems [124]. Reports from occupants usually encompass information and concerned details on occupants and their behaviors, and probably involve energy consumption and energy bills of buildings when the objective of research is to examine the effect of specific behavior on building energy consumption. 18
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Jia et al. (2017) summarized some of the state-of-the-art data collection technologies used for the research of occupant behavior [74], as shown in Figure 8. Cameras and passive infrared (PIR) sensors are commonly used for capturing the ground truth such as occupancy and occupant activities. Wireless Sensor Networks (WSN) and weather stations are the most popular avenues to gathering indoor and outdoor environmental data. Meters for electricity and gas are employed to provide information on energy consumption and usage patterns. Some other technologies like radio-frequency identification (RFID), ultra-wideband (UWB), Wi-Fi and customized sensors can be adopted based on specific research purposes.
19
ACCEPTED MANUSCRIPT
Figure 7 Technical framework of research on occupant behavior and building energy performance in literature
M
AN US
CR IP T
existing
Figure 8 State-of-the-art technologies for data collection in occupant behavior studies (adapted from [74])
AC
CE
PT
ED
Both monitoring methods and self-reports from occupants (Figure 7) have pros and cons. Monitoring methods require both the installation of devices, such as sensors and meters, and the involvement of technical experts, which may incur significant costs and take a long period of time. Thus, while it can provide data with finer granularity, it perhaps is more suitable for small scale research. Self-reports from occupants are easier to carry out, using either questionnaires or other survey tools, without large investments of money and experts‘ knowledge. While they may be more suitable for larger scale study, a mismatch may emerge between actual and reported information when self-reports are used for analysis, which may affect the accuracy of the conclusions. In practice, the purposes of research and available resources will determine which approach, or a hybrid of both, is used. 6. Quantitative modeling of occupant behavior and building energy performance Occupant behavior can be represented quantitatively at a certain level, despite its stochastic and complex nature, by means of mathematical models [41]. Occupant behavior can thus be integrated into building energy performance simulation and modeling to capture its impacts on energy and environmental performance of buildings [92]. Table 2 shows four categories of quantitative models found in the literature, using the way of categorization developed by Jia and co-authors [74, 125]. 20
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
It should be noted that such quantitative modeling methodologies often overlap to some degree, and can be combined in many ways for different research purposes. So the four categories presented below – stochastic/ probabilistic modelling, statistical methods, agent-based modeling and data-mining approaches – are not mutually exclusive, but they are commonly used in recent literature [29, 126, 127].
21
CR IP T
ACCEPTED MANUSCRIPT
Table 2. Four categories of quantitative models of occupant behavior (based on Ref. [74] and other references indicated)
Year
Building type
Technology
Variables
Stochastic/ probabilistic modeling
2011
University building
A system comprising 16 sensor nodes that consist of a camera with an adapter board
Record of transitions whenever a person crosses [128] a point.
2011
Office building
A wired sensor network comprising wireless sensor array; Cameras
CO2, temperature, TH, acoustics, lighting and motion detection; occupancy information
[129]
2014
Museum
Not reported
[130]
2015
Commercial
Wireless camera
Mean and variance of durations for occupants (assumed from museum open hours and Hong Kong building energy code) Occupancy data
2012
Not reported
Not reported
[132]
2014
Residential
Web-based survey
State of occupant behaviors with certain probability Individual attributes; household attribute; energy use per service unit
2017
Lab building
2017
Residential
Electricity meters
2014
Dormitory building
The program @RISK; electricity survey
M
ED
PT
CE AC
AN US
Methodology
A multi-layered supervised feedforward Artificial Neural Network (ANN);
22
Reference
[131]
[133]
building occupancy; presence/absence [134] (occupied status) of people in a building zone; number of occupants within the building zone; air change rates; number of people, appliances, lighting Electricity use; active occupancy; daily activity; [135] number and time of daily use of domestic appliances Occupancy; electricity use [136]
Questionnaire for occupant related parameters; temperature sensors for ambient monitor
2009
Office building
Occupancy sensors; digital photography; Indoor/outdoor environment data; occupant weather station presence intervals; positions and shades
[138]
2013
Residential
Ambient sensors, Hobo U9 sensors
[139]
2012 2010
Residential Office building
2015
Office building
2015
Office building
Indoor/outdoor environment parameters, window/door state, moment of the day, season, presence Heat sensors, power meters Indoor temperature; power usage Web-based survey Previous activity, working day, current activity, probability for next activity Self-developed door/window open status Six variables: Indoor/outdoor temperature and recording device humidity, wind speed, indoor CO2 concentration Wireless ceiling-mounted motion Occupancy state (duration) sensors
2012
Commercial
Not reported
Measured data: actual temperatures, zone schedules, occupant preferences, and occupant behaviors
[144]
2014
Residential
ABM built by REPAST Simphony
2 classes of agents, which include their own attributes
[145]
2014
Commercial
MATLAB for ABM; EnergyPlus for energy simulation
[146]
2015
Office building
No measured variables; 6 common behaviors that related to thermal comfort; all the input values are based on assumptions Local/global temperature; humidity; air velocity; heater use; window use
AC
Clothing and activity level, thermal sensation and preference, adaptive opportunities exercised; indoor temperature; local climate
ED
M
AN US
Office building
CE
Agent-based modeling
2008
PT
Statistical methods
CR IP T
ACCEPTED MANUSCRIPT
Online surveys; data loggers, power meters
23
[137]
[140] [141] [142]
[143]
[147]
[148]
Office building
Reasonable assumptions (of occupant Three categories of occupants; energy use rate; categories, occupancy related parameters factors that cause behavior change and occupants' interactions) Survey Occupancy schedule; simulated sensor data from other research Plugwise meter, pedometer Electricity consumption of office appliances, occupant behavior Not reported 10-min occupancy interval data
2010
Commercial
2014
Office building
2015 2015
Residential
Appliance-level power sensors
Power consumption user feedbacks
[152]
2014
Residential
Survey documented by occupants
[153]
2015
Residential
Temperature sensors, gas meters
2011
Residential
Field measurement (meters and sensors) and surveys
Self-reported activities on a minute by minute basis Indoor temperatures, gas consumption, time of furnace on/off state End-use loads (electricity, gas, kerosene); indoor temperature; lifestyle and related information of occupants
AN US
Commercial
AC
CE
PT
ED
M
Data-mining approaches
2012
CR IP T
ACCEPTED MANUSCRIPT
24
[149] [150] [151]
[154] [155]
ACCEPTED MANUSCRIPT
ED
M
AN US
CR IP T
a) Stochastic/probabilistic modeling. Stochastic/probabilistic models capture and describe the probability that specific behavior occurs based on historical or statistical data [156]. Generally, there are three types of stochastic/probabilistic occupant behavior models commonly used: Markov chain models [157], Bernoulli process [92], and survival analysis [158]. These models have been widely used to represent both occupancy and the actions occupants take to control their indoor environment. Markov chain models are a commonly-used type of probabilistic model. A Markov chain is a time series in which all states of the system can be directly observed and the future state depends only on the present state, independent of all past states. A Hidden Markov Model (HMM) assumes the possible states of a system are linked in a Markov chain but the states of the system are hidden from direct observation; instead, each system state is associated via a probability distribution with a set of observable parameters. Many researchers have used Markov chain models to describe occupancy status and behavior patterns. For example, Liisberg et al. (2016) [159] utilized HMMs to characterize occupant behavior based on indirect observations. Their investigation of average probability profiles as a function of time of day identified four distinct patterns of occupant behavior. Then based on the time of day dependence observed in their initial results, which used homogeneous HMMs, they developed time dependent HMMs (inhomogeneous HMMs) with improved outcomes of prediction. Markov chain models and HMMs are both stochastic processes. Bernoulli processes can be adopted when the probability of an event/state is not dependent on the previous state (i.e., memoryless), enabling the building energy modeling at the whole-building level [92]. Survival analysis is generally utilized to estimate the time duration of an event/state before a change happens, and can be used to estimate how long a building is likely to remain unchanged by occupants [92]. Due to the randomness and variability of occupant behavior, stochastic models are better in terms of validity and applicability for describing the actual interaction between occupants and building systems than deterministic models [105].
AC
CE
PT
b) Statistical methods. Statistical methods are generally carried out by building numerical relationship between occupant behavior and indoor/outdoor environment conditions, electricity usage or time periods, with the results being expressed by occupancy state or the probability of studied behavior [125]. Statistical analysis can be conducted to identify the patterns of behavior in buildings [160, 161]. One typical example is a 2008 study [137] in which logistic regression was utilized to investigate the effect of thermal stimuli on different types of behavior, such as opening/closing windows, blinds and doors. The researchers found that indoor temperature was a better predictor than outdoor temperature for these occupant behaviors. Statistical methods can better capture and describe occupant behavior in reality, but they require a larger sample size and data input of many variables, which could lead to a higher cost of field studies. c) Agent-based modeling. Agent-based modeling (ABM) uses multi-agent systems, comprised of autonomous agents, to simulate agents‘ interactions with each other and their external environments under specified rules [162]. The rules are critical to the simulation as they define how agents interact with each other and their environment. An example of the application of ABM to occupant-building interaction is Lee and Malkawi‘s (2014) simulation of multiple occupant behaviors in a commercial building [146]. They examined five specific behaviors: adjust clothing level, adjust activity level, window use, blind use, and space heater/personal fan use, as well as studying 25
ACCEPTED MANUSCRIPT how an agent adapts to the dynamic thermal changes in its space to optimize both comfort and energy savings. ABM has unique advantages in modeling occupant behavior as it introduces uncertainties like those in the real world and can describe the dynamics of human-building systems. Moreover, this approach allows the integration of models for both occupant behavior and building energy performance, which could be used in a combined interactive simulation of behavior and energy in buildings. Despite some improvements in the methodology of ABM, more comprehensive and holistic models are needed for further research in future.
ED
M
AN US
CR IP T
d) Data-mining approaches. Data-mining has been used in many recent studies on occupant behavior [151, 154, 163]. They require large databases and huge data storage. The application of data-mining to modeling occupant behavior follows its increasing use in studies of building energy performance as large volumes of data on building energy use and the energy consumed by specific appliances in buildings have become available. Zhao et al. (2014) developed an "indirect" practical data mining approach using office appliance power consumption data as a proxy for occupants‘ "passive" behavior [150]. They found that the average percentage of correctly classified individual behavior instances was 90.29% and their experimental result showed a fairly consistent group occupancy schedule, while capturing the diversity of individual behavior in using office appliances. While a high level of accuracy of behavior pattern prediction can be achieved, the application of this category of methods has been limited to occupancy and appliance use in residential buildings, possibly as a result of insufficient data and restricted access to other behavior and energy use data. For most studies to date, only household electricity use data has been employed for data-mining analysis of occupant behavior patterns. Data mining approaches are intended to overcome the shortcomings of the aforementioned traditional methods, particularly when dealing with big data streams, by offering reliable models of occupant behavior with the potential for rapid analysis and high replication [41, 150, 151].
PT
7. Energy-saving potential of occupant behavior in building energy performance
AC
CE
The critical role of occupant behavior in potential energy savings for the building sector has been recognized by many studies, and is supported by increased evidence presented by a series of IPCC reports [10, 164, 165]. According to the experiments and surveys in literature, behavioral change can reduce energy use by 6% to 25% [21, 101, 166, 167] for residential buildings, and 5% to 30% [167, 168] for commercial buildings. Some research [169] in the workplace indicated that group-level feedback and peer education could lead to a reduction of 8% and 4% in energy use, respectively. Ouyang and Hokao (2009) [48] and Winett et al. (1984) [170] both reported that the energy-saving potential by improving occupants‘ behavior in domestic life through energy-saving education can be more than 10% household electricity use. In the research of Ouyang and Hokao, a series of surveys about monthly electricity use, energy-saving education and household lifestyle were carried out in 124 households in three typical residential buildings in Hangzhou city of China. They examined the relationship between electricity consumption and household lifestyle, confirming that efforts to reduce residential energy use should be shifted from technological measures to improving occupants' behavior in ordinary domestic life. Wood and Newborough (2003) [101] experimented with electronic feedback via smart meters and displays, 26
ACCEPTED MANUSCRIPT
CR IP T
and found that 14 out of 31 households achieved energy savings of greater than 10% and six of these achieved savings of greater than 20%. The average reduction for households was 15%, whereas those given antecedent information (specifically referring to information about practical ways of reducing energy use in the form of pamphlets posted through the door, TV programs or the internet) alone reduced their electricity consumption by only 3% on average. Dennis et al. (1990) indicated that 60% reduction in unnecessary lighting use could be achieved by putting signs near light switches [171]. Table 3 summarizes the energy-saving potential of behavioral change in buildings. For example, it is possible to save 10-15% of the energy consumed for drying clothes if occupants use their clothes dryer at full load rather than at one-third to half load, and even save 100% of that if not using a clothes dryer (i.e. air drying inside when there is no space heating requirement, or outside) [10]. Table 3. Energy-saving potential of behavioral change in buildings
(Note: Proportions of energy consumption by end-use in residential and commercial buildings are based on Ref. [10])
Heating
32%
Hot water
24%
Cooling
2%
Cooking
29%
Lighting
4%
Refrigerators Clothes washers Clothes dryers
-
PT
Total for residential
9%
ED
Dishwashers
Total for commercial
-
Proportion in commercial
Potential energy savings from behavioral change
AN US
Proportion in residential
33%
10%-30%[10]; 51%[96]
12%
50%[10]
7%
50%-67%[10]
-
50%[10]
16%
60%[171]; 70%[10]
M
Energy use in buildings
-
30%[10]; 50%[10] 75%[10] 60%-85%[10] 10%-15%[10]; 10-100%[10]
-
6%-25%[101] [21] [166] [167]
-
5%-30%[167, 168]
AC
CE
It has been observed that some people tend to use energy-consuming devices in buildings more often and for a longer period of time when they learn those devices are energy efficient, which is referred to as the rebound effect and may hinder the realization of behavioral energy-saving potential [172, 173]. The rebound effect can result from the intensification of an activity by occupants. For example, the adoption of energy-efficient appliances does not necessarily result in a reduction of overall energy consumption if people use these appliances more often [174, 175]. Moreover, a behavioral energy conservation campaign conducted by Tiefenbeck et al. (2013) [176] in 154 apartments showed that residents who received weekly feedback on their water consumption lowered their water use (6.0% on average), but at the same time increased their electricity consumption by 5.6% compared with control subjects. This is probably caused by a warm glow effect of ―already doing something‖ among occupants, when they feel satisfied that they have done their part of water efficient actions and then tend to consume more energy in other places with heedless behaviors [177]. Midden et al. (2007) [178] illustrated that technology and behavior were 27
ACCEPTED MANUSCRIPT
CR IP T
closely interwoven in many respects, and indicated various ways in which technological and behavioral factors could be integrated in interactive approaches to effectively promote building energy conservation. Therefore, a more comprehensive framework of intervention programs and strategies targeted on occupant behavior should take the rebound effect into consideration to completely fulfil the energy-saving potential of behavioral change. Meanwhile, some researchers [179, 180] indicated that the rebound effect does exist in reality and should be considered in strategic energy planning, but in some way it is overplayed and can be diluted by other policy options aiming to reduce building energy use, such as stricter building energy codes. 8. Research gaps
Based on our review of the literature on occupant behavior and building energy performance, the following research gaps are identified to further improve building energy performance and reduce building energy consumption.
AN US
(1) Occupant behavior needs to be understood within a systematic framework of research.
AC
CE
PT
ED
M
To date, research on occupant behavior seems to have focused on several specific behaviors, such as window opening behavior, lighting control behavior and space heating/cooling, with few studies including all relevant kinds of occupant behavior into the scope of research. This means there is still room to improve our understanding of occupant behavior with regard to energy use in buildings. A clearer and consistent definition of occupant behavior that is shared by researchers from different disciplines is needed. There are some examples [181] where occupant behavior is narrowed down or treated as equal to occupancy, which is in fact only one element of occupant behavior, as indicated by Jia et al. [74]. Some types of energy-related activities such as television and computer uses in buildings are also relatively rarely examined in studies. Meanwhile, some studies estimated the energy-saving potential from behavioral change, but often focusing on one or two particular types of energy-related behavior, making it difficult to compare results and findings across case studies. This might lead to an underestimation of the overall impact of occupant behavior on building energy performance. Additionally, a systematic framework will enable researchers to model and capture the interaction between occupants and building energy systems from different perspectives with a unified evaluation system. Thus, a more systematic framework of research to define the boundaries of occupant behavior, with all critical elements and links included and clearly described, is urgently needed. (2) Empirical evidence and data at a larger city scale are needed. A significant shortcoming of occupant behavior research is that there are few data on behavior profiles and energy use in real-world buildings. In order to improve outcomes of simulation models, sufficient empirical data are needed to adjust and validate the modeling techniques. Some researchers adopted the data from national time use surveys as a proxy for actual behavior profiles [182, 183]. Empirical evidence and data are critical to occupant behavior and building energy performance 28
ACCEPTED MANUSCRIPT
CR IP T
research and more field surveys should be conducted to enhance existing studies as well as facilitate further analysis in this field. The US national surveys (RECS & CBECS) [184] on energy consumption of residential and commercial buildings have explored and collected data of building characteristics, ownership and operating frequency of energy-consuming equipment and appliances, as well as occupancy and energy bills, missing occupants‘ reaction to indoor discomforts, which is a critical element in occupant behavior research. Rapid urbanization and associated lifestyle change in developing countries like China mean more energy could be consumed in residential buildings of urban areas [185, 186]. Understanding building energy performance beyond individual buildings and at a larger city scale is important for city scale policy making and implementation. However, based on our dataset of literature, little research explores occupant behavior in association with building energy performance at such scale. Furthermore, a comparison with respect to occupant behavior patterns and building energy performances between different cities could add another layer of important insight, but no such work has been seen yet.
AN US
(3) Occupant behavior needs to be understood in the context of socio-economic status and energy policy.
AC
CE
PT
ED
M
Much of the literature on simulation and modeling of occupant behavior puts almost exclusive focus on connecting occupant behavior with natural environmental factors, building characteristics and personal profiles, and largely leaving out the potential impact of socio-economic status, which can be a critical influencing factor and particularly important in large scale empirical studies. The socio-economic context would supplement existing understanding of occupant behavior and could inform better strategies for encouraging more energy efficient behavior. Allcott and Mullainathan (2010) [22] have demonstrated the critical role of behavior in energy policy; equally, it is crucial for policymakers to incorporate behavioral elements in the policy framework of building energy efficiency, just as this paper has indicated. More research focus should be put on incorporating behavioral factors into energy policy-making. For example, some intervention strategies such as information programs for residents should be considered to complement existing building energy policies. Evidence has shown that behavior-based policies (such as energy use feedback programs) could reduce household energy consumption with little financial investment by either governments or households [54]. Besides, single antecedent-based intervention proved to be not effective as expected in some cases [187], and demographics and motivations of people in different contexts also have to be included in the policy-making of intervention programs for energy efficient behavior. (4) The role of occupant behavior in the effectiveness of building energy efficiency policy remains unclear. Building energy efficiency policy and its outcome is complex and multifaceted. Its effectiveness can be influenced by various factors such as response of the target group to policy, the conditions of stakeholders, the design and implementation process of policy, etcetera. The role of occupant behavior in the effectiveness of building energy efficiency policy is a big research gap. By identifying occupant behavior‘s influence on the effectiveness of relevant policies, policymakers could adjust and improve 29
ACCEPTED MANUSCRIPT building energy efficiency policy for better outcomes.
9. Conclusions
Acknowledgements
ED
M
AN US
CR IP T
Occupant behavior is critical to the energy performance of buildings, which can be an important supplement to the technology approach to improving building energy performance. Recent years have seen an exponential growth in literature focusing on occupant behavior. This paper identified the hot topics, overall research trends, and remaining research gaps by means of bibliometric analysis and in-depth review of literature to date. Four hot-topic research areas have been identified, namely 1) understanding of occupant behavior, especially specific behaviors affecting indoor thermal comfort, such as window opening behavior, lighting control behavior and space heating/cooling behavior; 2) research methods and energy data collection; 3) quantitative modeling of occupant behavior and building energy performance; and 4) energy saving potential and behavioral strategies. Keywords analysis showed a shift of research focus from qualitative research to quantitative analysis. There are four main types of quantitative methods used in modeling occupant behavior: probabilistic modeling, statistical methods, agent-based modeling, and data-mining approaches. The energy-saving potential of occupant behavior in building energy performance ranges between 10%-25% for residential buildings, and 5%-30% for commercial buildings. We concluded by identifying four significant research gaps about occupant behavior and building energy performance: the need for understanding occupant behavior in a systematic framework; for stronger empirical evidence beyond individual buildings and at a larger city scale; for linking occupant behavior to socio-economic and policy variables; and for evaluating the role of occupant behavior in the effectiveness of building energy efficiency policy.
References
PT
This work was partly funded by the China Scholarship Council (CSC) under the Grant No. 201506030015.
AC
CE
[1] A.M. Kazim, Assessments of primary energy consumption and its environmental consequences in the United Arab Emirates, Renewable and Sustainable Energy Reviews, 11 (3) (2007) 426-446. [2] F. Lima, M.L. Nunes, J. Cunha, A.F. Lucena, Driving forces for aggregate energy consumption: A cross-country approach, Renewable and Sustainable Energy Reviews, 68 (2017) 1033-1050. [3] D.R. Easterling, G.A. Meehl, C. Parmesan, S.A. Changnon, T.R. Karl, L.O. Mearns, Climate Extremes: Observations, Modeling, and Impacts, Science, 289 (2000) 2068-2074. [4] M. Reichstein, M. Bahn, P. Ciais, D. Frank, M.D. Mahecha, S.I. Seneviratne, J. Zscheischler, C. Beer, N. Buchmann, D.C. Frank, D. Papale, A. Rammig, P. Smith, K. Thonicke, M. van der Velde, S. Vicca, A. Walz, M. Wattenbach, Climate extremes and the carbon cycle, Nature, 500 (7462) (2013) 287-295. [5] C. Spandagos, T.L. Ng, Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities, Applied Energy, 189 (2017) 352-368. [6] L. Steg, G. Perlaviciute, E. van der Werff, Understanding the human dimensions of a sustainable energy transition, Frontiers in psychology, 6 (2015). [7] M.K. Dixit, J.L. Fernández-Solís, S. Lavy, C.H. Culp, Identification of parameters for embodied energy measurement: A literature review, Energy and Buildings, 42 (8) (2010) 1238-1247. [8] Y. Zhang, C.-Q. He, B.-J. Tang, Y.-M. Wei, China's energy consumption in the building sector: A life cycle approach, Energy and Buildings, 94 (2015) 240-251. [9] E.I.A. (EIA), International Energy Outlook 2016, in, U.S. Department of Energy, May 2016, pp. 30
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
DOE/EIA-0484(2016). [10] O. Lucon, D. Ürge-Vorsatz, A.Z. Ahmed, H. Akbari, P. Bertoldi, L.F. Cabeza, N. Eyre, A. Gadgil, L.D. Harvey, Y. Jiang, E. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C. Pyke, M.V. VilariÒo, Chapter 9 - Buildings, in: Climate Change 2014: Mitigation of Climate Change. IPCC Working Group III Contribution to AR5, Cambridge University Press, 2014. [11] L.D. Harvey, Reducing energy use in the buildings sector: measures, costs, and examples, Energy Efficiency, 2 (2) (2009) 139-163. [12] M. Levine, D. Ürge-Vorsatz, K. Blok, L. Geng, D. Harvey, S. Lang, G. Levermore, A. Mongameli Mehlwana, S. Mirasgedis, A. Novikova, J. Rilling, Y. H., Residential and commercial buildings, in: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2007. [13] M. Asif, Growth and sustainability trends in the buildings sector in the GCC region with particular reference to the KSA and UAE, Renewable and Sustainable Energy Reviews, 55 (2016) 1267-1273. [14] L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and buildings, 40 (3) (2008) 394-398. [15] I.E.A. (IEA), Energy in Buildings and Communities Programme Annual Report 2016, in, IEA-EBC, 2017. [16] International Energy Agency, Technology Roadmap: Energy Efficient Building Envelopes, in, International Energy Agency, 2013. [17] W. Goetzler, M. Guernsey, J. Young, Research & Development Roadmap for Emerging HVAC Technologies, in, 2014. [18] J.C. Lam, Energy analysis of commercial buildings in subtropical climates, Building and Environment, 35 (1) (2000) 19-26. [19] N. Aste, M. Manfren, G. Marenzi, Building Automation and Control Systems and performance optimization: A framework for analysis, Renewable and Sustainable Energy Reviews, (2016). [20] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renewable and Sustainable Energy Reviews, 34 (2014) 409-429. [21] M. Pothitou, A.J. Kolios, L. Varga, S. Gu, A framework for targeting household energy savings through habitual behavioural change, International Journal of Sustainable Energy, 35 (7) (2016) 686-700. [22] H. Allcott, S. Mullainathan, Behavior and energy policy, Science, 327 (5970) (2010) 1204-1205. [23] H. Allcott, M. Greenstone, Is there an energy efficiency gap?, The Journal of Economic Perspectives, 26 (1) (2012) 3-28. [24] N. Kahma, K. Matschoss, The rejection of innovations? Rethinking technology diffusion and the non-use of smart energy services in Finland, Energy Res. Soc. Sci., 34 (2017) 27-36. [25] G. Branco, B. Lachal, P. Gallinelli, W. Weber, Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data, Energy and Buildings, 36 (6) (2004) 543-555. [26] R.H. Socolow, The Twin Rivers program on energy conservation in housing: Highlights and conclusions, Energy and Buildings, 1 (3) (1978) 207-242. [27] G. Levermore, Monitoring and targeting; motivation and training, in: Energy Management Experience Conference, CICC, Cambridge, UK, 1985, pp. 21-30. [28] M. Schweiker, A. Wagner, The effect of occupancy on perceived control, neutral temperature, and behavioral patterns, Energy and Buildings, 117 (2016) 246-259. [29] S. D‘Oca, T. Hong, J. Langevin, The human dimensions of energy use in buildings: A review, Renewable and Sustainable Energy Reviews, 81 (2018) 731-742. [30] M.C. Gonzalez, C.A. Hidalgo, A.-L. Barabasi, Understanding individual human mobility patterns, nature, 453 (7196) (2008) 779. [31] P.C. Stern, B.K. Sovacool, T. Dietz, Towards a science of climate and energy choices, Nature Climate Change, (2016). [32] J. Schot, L. Kanger, G. Verbong, The roles of users in shaping transitions to new energy systems, Nature Energy, 1 (2016) 16054. [33] T. Dietz, G.T. Gardner, J. Gilligan, P.C. Stern, M.P. Vandenbergh, Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions, Proceedings of the National Academy of Sciences, 106 (44) (2009) 18452-18456. [34] M. Lopes, C. Antunes, N. Martins, Energy behaviours as promoters of energy efficiency: A 21st century review, Renewable and Sustainable Energy Reviews, 16 (6) (2012) 4095-4104. [35] T. Dietz, P.C. Stern, E.U. Weber, Reducing carbon-based energy consumption through changes in household behavior, Daedalus, 142 (1) (2013) 78-89. 31
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
[36] T. Hong, S. D'Oca, W.J. Turner, S.C. Taylor-Lange, An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework, Building and Environment, 92 (2015) 764-777. [37] X. Bai, R.J. Dawson, D. Ürge-Vorsatz, G.C. Delgado, A.S. Barau, S. Dhakal, D. Dodman, L. Leonardsen, V. Masson-Delmotte, D. Roberts, Six research priorities for cities and climate change, Nature, 555 (7694) (2018) 23-25. [38] D. Ürge-Vorsatz, C. Rosenzweig, R.J. Dawson, R.S. Rodriguez, X. Bai, A.S. Barau, K.C. Seto, S. Dhakal, Locking in positive climate responses in cities, Nature Climate Change, 8 (3) (2018) 174. [39] D. Ürge-Vorsatz, N. Eyre, P. Graham, D. Harvey, E. Hertwich, Y. Jiang, C. Kornevall, M. Majumdar, J. McMahon, S. Mirasgedis, Chapter 10-energy end-use: building, Global Energy Assessment-Toward a Sustainable Future, (2012) 649-760. [40] A. Grubler, X. Bai, T. Buettner, S. Dhakal, D.J. Fisk, T. Ichinose, J.E. Keirstead, G. Sammer, D. Satterthwaite, N.B. Schulz, N. Shah, J. Steinberger, H. Weisz, Chapter 18 - Urban Energy Systems, in: Global Energy Assessment - Toward a Sustainable Future, Cambridge University Press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria, 2012, pp. 1307-1400. [41] T. Hong, D. Yan, S. D'Oca, C.-f. Chen, Ten questions concerning occupant behavior in buildings: The big picture, Building and Environment, 114 (2017) 518-530. [42] R.K. Jain, R. Gulbinas, J.E. Taylor, P.J. Culligan, Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behavior of networked users exposed to normative eco-feedback, Energy and Buildings, 66 (2013) 119-127. [43] L. Steg, Promoting household energy conservation, Energy Policy, 36 (12) (2008) 4449-4453. [44] F. Stazi, F. Naspi, M. D'Orazio, A literature review on driving factors and contextual events influencing occupants' behaviours in buildings, Building and Environment, 118 (2017) 40-66. [45] A. Roetzel, A. Tsangrassoulis, U. Dietrich, S. Busching, A review of occupant control on natural ventilation, Renew. Sust. Energ. Rev., 14 (3) (2010) 1001-1013. [46] V. Fabi, R.V. Andersen, S. Corgnati, B.W. Olesen, Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models, Building and Environment, 58 (2012) 188-198. [47] A.L. Pisello, V.L. Castaldo, C. Piselli, C. Fabiani, F. Cotana, How peers' personal attitudes affect indoor microclimate and energy need in an institutional building: Results from a continuous monitoring campaign in summer and winter conditions, Energy and Buildings, 126 (2016) 485-497. [48] J.L. Ouyang, K. Hokao, Energy-saving potential by improving occupants' behavior in urban residential sector in Hangzhou City, China, Energy and Buildings, 41 (7) (2009) 711-720. [49] G. Peschiera, J.E. Taylor, J.A. Siegel, Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data, Energy and Buildings, 42 (8) (2010) 1329-1336. [50] G. Peschiera, J.E. Taylor, The impact of peer network position on electricity consumption in building occupant networks utilizing energy feedback systems, Energy and Buildings, 49 (2012) 584-590. [51] J. Chen, J.E. Taylor, H.-H. Wei, Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation, Energy and Buildings, 47 (2012) 515-524. [52] J. Chen, R.K. Jain, J.E. Taylor, Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption, Applied energy, 105 (2013) 358-368. [53] R.M. Tetlow, C.P. Beaman, A.A. Elmualim, K. Couling, Simple prompts reduce inadvertent energy consumption from lighting in office buildings, Building and Environment, 81 (2014) 234-242. [54] M.A. Delmas, M. Fischlein, O.I. Asensio, Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012, Energy Policy, 61 (2013) 729-739. [55] Q.D. Li, X. Sun, C. Chen, X.D. Yang, Characterizing the household energy consumption in heritage Nanjing Tulou buildings, China: A comparative field survey study, Energy and Buildings, 49 (2012) 317-326. [56] P. Jiang, Y.H. Chen, B. Xu, W.B. Dong, E. Kennedy, Building low carbon communities in China: The role of individual's behaviour change and engagement, Energy Policy, 60 (2013) 611-620. [57] S. Hu, D. Yan, S. Guo, Y. Cui, B. Dong, A survey on energy consumption and energy usage behavior of households and residential building in urban China, Energy and Buildings, (2017). [58] S. Marzban, L. Ding, V. Timchenko, M. Irger, Façade Optimization in a Wind-Driven Ventilated Residential Building Targeting Thermal Comfort, IAQ and Energy Consumption, International Journal of Environmental Science and Development, 7 (5) (2016) 379. 32
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
[59] W. Zhao, L. Ding, P. Cooper, P. Perez, Smart home electricity management in the context of local power resources and smart grid, (2014). [60] X. Zhou, D. Yan, X. Feng, G. Deng, Y. Jian, Y. Jiang, Influence of household air-conditioning use modes on the energy performance of residential district cooling systems, Build. Simul., 9 (4) (2016) 429-441. [61] G.Y. Yun, J. Choi, J.T. Kim, Energy performance of direct expansion air handling unit in office buildings, Energy and Buildings, 77 (2014) 425-431. [62] G.Y. Yun, K. Steemers, N. Baker, Natural ventilation in practice: linking facade design, thermal performance, occupant perception and control, Build. Res. Informat., 36 (6) (2008) 608-624. [63] A. Stafford, D. Lilley, Predicting in situ heat pump performance: An investigation into a single ground-source heat pump system in the context of 10 similar systems, Energy and Buildings, 49 (2012) 536-541. [64] M. Scarpa, U. Mazzali, F. Peron, Modeling the energy performance of living walls: Validation against field measurements in temperate climate, Energy and Buildings, 79 (2014) 155-163. [65] D. Saelens, W. Parys, R. Baetens, Energy and comfort performance of thermally activated building systems including occupant behavior, Building and Environment, 46 (4) (2011) 835-848. [66] A. Roetzel, A. Tsangrassoulis, U. Dietrich, Impact of building design and occupancy on office comfort and energy performance in different climates, Building and Environment, 71 (2014) 165-175. [67] O. Mejri, E.P. Del Barrio, N. Ghrab-Morcos, Energy performance assessment of occupied buildings using model identification techniques, Energy and Buildings, 43 (2-3) (2011) 285-299. [68] A. Ioannou, L.C.M. Itard, Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy, Energy and Buildings, 92 (2015) 216-233. [69] M. Sunikka-Blank, R. Galvin, Introducing the prebound effect: the gap between performance and actual energy consumption, Build. Res. Informat., 40 (3) (2012) 260-273. [70] D.A. Majcen, L. Itard, H. Visscher, Statistical model of the heating prediction gap in Dutch dwellings: Relative importance of building, household and behavioural characteristics, Energy and Buildings, 105 (2015) 43-59. [71] I. Gaetani, P.J. Hoes, J.L.M. Hensen, Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy, Energy and Buildings, 121 (2016) 188-204. [72] L.K. Norford, R.H. Socolow, E.S. Hsieh, G.V. Spadaro, 2-TO-ONE DISCREPANCY BETWEEN MEASURED AND PREDICTED PERFORMANCE OF A LOW-ENERGY OFFICE BUILDING INSIGHTS FROM A RECONCILIATION BASED ON THE DOE-2 MODEL, Energy and Buildings, 21 (2) (1994) 121-131. [73] E. Azar, C.C. Menassa, Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings, J. Comput. Civil. Eng., 26 (4) (2012) 506-518. [74] M.D. Jia, R.S. Srinivasan, A.A. Raheem, From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency, Renew. Sust. Energ. Rev., 68 (2017) 525-540. [75] A.C. Menezes, A. Cripps, D. Bouchlaghem, R. Buswell, Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap, Applied Energy, 97 (2012) 355-364. [76] K. Schakib-Ekbatan, F.Z. Cakici, M. Schweiker, A. Wagner, Does the occupant behavior match the energy concept of the building? - Analysis of a German naturally ventilated office building, Building and Environment, 84 (2015) 142-150. [77] U.I. Dar, L. Georges, I. Sartori, V. Novakovic, Influence of occupant's behavior on heating needs and energy system performance: A case of well-insulated detached houses in cold climates, Build. Simul., 8 (5) (2015) 499-513. [78] C.L. Borgman, J. Furner, Scholarly communication and bibliometrics, Annual review of information science and technology, 36 (1) (2002) 2-72. [79] T.U. Daim, G. Rueda, H. Martin, P. Gerdsri, Forecasting emerging technologies: Use of bibliometrics and patent analysis, Technological Forecasting and Social Change, 73 (8) (2006) 981-1012. [80] Y.-M. Wei, Z.-F. Mi, Z. Huang, Climate policy modeling: an online SCI-E and SSCI based literature review, Omega, 57 (2015) 70-84. [81] H. Yu, Y.-M. Wei, B.-J. Tang, Z. Mi, S.-Y. Pan, Assessment on the research trend of low-carbon energy technology investment: A bibliometric analysis, Applied energy, 184 (2016) 960-970. [82] R.H. Socolow, TWIN RIVERS PROGRAM ON ENERGY-CONSERVATION IN HOUSING HIGHLIGHTS AND CONCLUSIONS, Energy and Buildings, 1 (3) (1978) 207-242. [83] C. Seligman, J.M. Darley, L.J. Becker, Behavioral approaches to residential energy conservation, 33
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Energy and buildings, 1 (3) (1978) 325-337. [84] R.C. Sonderegger, Movers and stayers: The resident's contribution to variation across houses in energy consumption for space heating, Energy and Buildings, 1 (3) (1978) 313-324. [85] D. Yan, T. Hong, IEA EBC Annex 66: Definition and Simulation of Occupant Behavior in Buildings, in, 2013. [86] P. Hoes, J.L.M. Hensen, M. Loomans, B. de Vries, D. Bourgeois, User behavior in whole building simulation, Energy and Buildings, 41 (3) (2009) 295-302. [87] Z. Yu, B.C.M. Fung, F. Haghighat, H. Yoshino, E. Morofsky, A systematic procedure to study the influence of occupant behavior on building energy consumption, Energy and Buildings, 43 (6) (2011) 1409-1417. [88] I. Richardson, M. Thomson, D. Infield, A high-resolution domestic building occupancy model for energy demand simulations, Energy and Buildings, 40 (8) (2008) 1560-1566. [89] J. Widen, E. Wackelgard, A high-resolution stochastic model of domestic activity patterns and electricity demand, Applied Energy, 87 (6) (2010) 1880-1892. [90] D. Bourgeois, C. Reinhart, I. Macdonald, Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control, Energy and Buildings, 38 (7) (2006) 814-823. [91] R.V. Jones, A. Fuertes, E. Gregori, A. Giretti, Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings, Building and Environment, 118 (2017) 144-158. [92] D. Yan, W. O'Brien, T.Z. Hong, X.H. Feng, H.B. Gunay, F. Tahmasebi, A. Mahdavi, Occupant behavior modeling for building performance simulation: Current state and future challenges, Energy and Buildings, 107 (2015) 264-278. [93] S.Q. Chen, W.W. Yang, H. Yoshino, M.D. Levine, K. Newhouse, A. Hinge, Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies, Energy and Buildings, 104 (2015) 1-13. [94] M. Costanzo, D. Archer, E. Aronson, T. Pettigrew, Energy conservation behavior: The difficult path from information to action, American psychologist, 41 (5) (1986) 521. [95] R.V. Andersen, J. Toftum, K.K. Andersen, B.W. Olesen, Survey of occupant behaviour and control of indoor environment in Danish dwellings, Energy and Buildings, 41 (1) (2009) 11-16. [96] Z.M. Gill, M.J. Tierney, I.M. Pegg, N. Allan, Low-energy dwellings: the contribution of behaviours to actual performance, Build. Res. Informat., 38 (5) (2010) 491-508. [97] H.B. Gunay, W. O'Brien, I. Beausoleil-Morrison, A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices, Building and Environment, 70 (2013) 31-47. [98] S. Gauthier, Investigating the probability of behavioural responses to cold thermal discomfort, Energy and Buildings, 124 (2016) 70-78. [99] H. Polinder, M. Schweiker, A. van der Aa, K. Schakib-Ekbatan, V. Fabi, R. Andersen, Final Report Annex 53, Volume II Occupant Behavior and Modeling, Retrieved from Tohoku http:// www. iea-ebc. org/ index. php, (2013). [100] G.T. Gardner, P.C. Stern, Environmental problems and human behavior, Allyn & Bacon, 1996. [101] G. Wood, M. Newborough, Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design, Energy and buildings, 35 (8) (2003) 821-841. [102] J. Langevin, P.L. Gurian, J. Wen, Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors, Applied Energy, 102 (2013) 1358-1370. [103] D. Cali, R.K. Andersen, D. Mueller, B.W. Olesen, Analysis of occupants' behavior related to the use of windows in German households, Building and Environment, 103 (2016) 54-69. [104] Y.W. Jian, Y.J. Guo, J. Liu, Z. Bai, Q.R. Li, Case study of window opening behavior using field measurement results, Build. Simul., 4 (2) (2011) 107-116. [105] V. Fabi, R.K. Andersen, S. Corgnati, Verification of stochastic behavioural models of occupants' interactions with windows in residential buildings, Building and Environment, 94 (2015) 371-383. [106] V. Fabi, R.K. Andersen, S. Corgnati, Accounting for the Uncertainty Related to Building Occupants with Regards to Visual Comfort: A Literature Survey on Drivers and Models, Buildings, 6 (1) (2016) 17. [107] F. Haldi, D. Robinson, The impact of occupants' behaviour on building energy demand, J. Build. Perf. Simul., 4 (4) (2011) 323-338. [108] C.C. Menassa, N. Taylor, J. Nelson, Optimizing hybrid ventilation in public spaces of complex buildings–a case study of the Wisconsin Institutes for Discovery, Building and Environment, 61 (2013) 57-68. [109] S.P. Corgnati, S. D'Oca, V. Fabi, R.K. Andersen, Leverage of Behavioural Patterns of Window 34
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Opening and Heating Set Point Adjustments on Energy Consumption and Thermal Comfort in Residential Buildings, in: A. Li, Y. Zhu, Y. Li (Eds.) Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning, Vol 1: Indoor and Outdoor Environment, Vol. 261, Springer-Verlag Berlin, Berlin, 2014, pp. 23-31. [110] L.P. Wang, S. Greenberg, Window operation and impacts on building energy consumption, Energy and Buildings, 92 (2015) 313-321. [111] A. Heydarian, E. Pantazis, J.P. Carneiro, D. Gerber, B. Becerik-Gerber, Lights, building, action: Impact of default lighting settings on occupant behaviour, J. Environ. Psychol., 48 (2016) 212-223. [112] A. Heydarian, J.P. Carneiro, D. Gerber, B. Becerik-Gerber, Immersive virtual environments, understanding the impact of design features and occupant choice upon lighting for building performance, Building and Environment, 89 (2015) 217-228. [113] C.E. Ochoa, M.B.C. Aries, J.L.M. Hensen, State of the art in lighting simulation for building science: a literature review, J. Build. Perf. Simul., 5 (4) (2012) 209-233. [114] G.Y. Yun, H. Kim, J.T. Kim, Effects of occupancy and lighting use patterns on lighting energy consumption, Energy and Buildings, 46 (2012) 152-158. [115] G.Y. Yun, H.J. Kong, H. Kim, J.T. Kim, A field survey of visual comfort and lighting energy consumption in open plan offices, Energy and Buildings, 46 (2012) 146-151. [116] X. Zhou, D. Yan, T.Z. Hong, X.X. Ren, Data analysis and stochastic modeling of lighting energy use in large office buildings in China, Energy and Buildings, 86 (2015) 275-287. [117] H.B. Gunay, W. O'Brien, I. Beausoleil-Morrison, S. Gilani, Development and implementation of an adaptive lighting and blinds control algorithm, Building and Environment, 113 (2017) 185-199. [118] C. Wang, D. Yan, X. Ren, Modeling Individual's Light Switching Behavior to Understand Lighting Energy Use of Office Building, Energy Procedia, 88 (2016) 781-787. [119] A.D. Galasiu, M.R. Atif, Applicability of daylighting computer modeling in real case studies: comparison between measured and simulated daylight availability and lighting consumption, Building and Environment, 37 (4) (2002) 363-377. [120] D.H. Li, J.C. Lam, Evaluation of lighting performance in office buildings with daylighting controls, Energy and Buildings, 33 (8) (2001) 793-803. [121] D. Lindelöf, N. Morel, A field investigation of the intermediate light switching by users, Energy and Buildings, 38 (7) (2006) 790-801. [122] D. Ürge-Vorsatz, L.F. Cabeza, S. Serrano, C. Barreneche, K. Petrichenko, Heating and cooling energy trends and drivers in buildings, Renewable and Sustainable Energy Reviews, 41 (2015) 85-98. [123] Z. Wang, Z. Zhao, B.R. Lin, Y.X. Zhu, Q. Ouyang, Residential heating energy consumption modeling through a bottom-up approach for China's Hot Summer-Cold Winter climatic region, Energy and Buildings, 109 (2015) 65-74. [124] B. Dong, M.B. Kjærgaard, M.D. Simone, H.B. Gunay, W. O‘Brien, D. Mora, J. Dziedzic, J. Zhao, Sensing and Data Acquisition, in: W. Andreas, W. O‘Brien, B. Dong. (Eds.) Exploring Occupant Behavior in Buildings: Methods and Challenges, Springer International Publishing AG, 2017. [125] M. Jia, R.S. Srinivasan, Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies, in: Proceedings of the 2015 Winter Simulation Conference, IEEE Press, 2015, pp. 3345-3355. [126] V.L. Castaldo, A.L. Pisello, Uses of dynamic simulation to predict thermal‐ energy performance of buildings and districts: a review, Wiley Interdisciplinary Reviews: Energy and Environment, 7 (1) (2018). [127] P.X. Zou, X. Xu, J. Sanjayan, J. Wang, A mixed methods design for building occupants‘ energy behavior research, Energy and Buildings, 166 (2018) 239-249. [128] V.L. Erickson, M.Á. Carreira-Perpiñán, A.E. Cerpa, OBSERVE: Occupancy-based system for efficient reduction of HVAC energy, in: Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on, IEEE, 2011, pp. 258-269. [129] B. Dong, K.P. Lam, Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network, J. Build. Perf. Simul., 4 (4) (2011) 359-369. [130] C. Lee, J. Tong, V. Cheng, Occupant behavior in building design and operation, in: HK joint Symposium 2014, 2014. [131] Z. Chen, J. Xu, Y.C. Soh, Modeling regular occupancy in commercial buildings using stochastic models, Energy and Buildings, 103 (2015) 216-223. [132] J. Virote, R. Neves-Silva, Stochastic models for building energy prediction based on occupant behavior assessment, Energy and Buildings, 53 (2012) 183-193. [133] T. Zaraket, B. Yannou, Y. Leroy, S. Minel, E. Chapotot, A Stochastic activity-based approach for forecasting occupant-related energy consumption in residential buildings, in: ASME 2014 35
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2014, pp. V004T006A034-V004T006A034. [134] F.R. Cecconi, M. Manfren, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis, Probabilistic behavioural modeling in building performance simulation: A Monte Carlo approach, Energy and Buildings, (2017). [135] G. Flett, N. Kelly, A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand, Energy and Buildings, 140 (2017) 171-187. [136] C.M. Stoppel, F. Leite, Integrating probabilistic methods for describing occupant presence with building energy simulation models, Energy and Buildings, 68 (2014) 99-107. [137] F. Haldi, D. Robinson, On the behaviour and adaptation of office occupants, Building and environment, 43 (12) (2008) 2163-2177. [138] A. Mahdavi, C. Pröglhöf, User behavior and energy performance in buildings, Wien, Austria: Internationalen Energiewirtschaftstagung an Der TU Wien (IEWT), (2009) 1-13. [139] R. Andersen, V. Fabi, J. Toftum, S.P. Corgnati, B.W. Olesen, Window opening behaviour modelled from measurements in Danish dwellings, Building and Environment, 69 (2013) 101-113. [140] C. Peng, D. Yan, R.H. Wu, C. Wang, X. Zhou, Y. Jiang, Quantitative description and simulation of human behavior in residential buildings, Build. Simul., 5 (2) (2012) 85-94. [141] V. Tabak, B. de Vries, Methods for the prediction of intermediate activities by office occupants, Building and Environment, 45 (6) (2010) 1366-1372. [142] N. Li, J.C. Li, R.J. Fan, H.Y. Jia, Probability of occupant operation of windows during transition seasons in office buildings, Renew. Energy, 73 (2015) 84-91. [143] A. Mahdavi, F. Tahmasebi, Predicting people's presence in buildings: An empirically based model performance analysis, Energy and Buildings, 86 (2015) 349-355. [144] L. Klein, J.-y. Kwak, G. Kavulya, F. Jazizadeh, B. Becerik-Gerber, P. Varakantham, M. Tambe, Coordinating occupant behavior for building energy and comfort management using multi-agent systems, Automation in Construction, 22 (2012) 525-536. [145] A. Alfakara, B. Croxford, Using agent-based modelling to simulate occupants' behaviours in response to summer overheating, in: Proceedings of the Symposium on Simulation for Architecture & Urban Design, Society for Computer Simulation International, 2014, pp. 13. [146] Y.S. Lee, A.M. Malkawi, Simulating multiple occupant behaviors in buildings: An agent-based modeling approach, Energy and Buildings, 69 (2014) 407-416. [147] J. Langevin, J. Wen, P.L. Gurian, Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors, Building and Environment, 88 (2015) 27-45. [148] E. Azar, C.C. Menassa, Agent-based modeling of occupants and their impact on energy use in commercial buildings, J. Comput. Civil. Eng., 26 (4) (2011) 506-518. [149] C. Liao, P. Barooah, An integrated approach to occupancy modeling and estimation in commercial buildings, in: American Control Conference (ACC), 2010, IEEE, 2010, pp. 3130-3135. [150] J. Zhao, B. Lasternas, K.P. Lam, R. Yun, V. Loftness, Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining, Energy and Buildings, 82 (2014) 341-355. [151] S. D'Oca, T.Z. Hong, Occupancy schedules learning process through a data mining framework, Energy and Buildings, 88 (2015) 395-408. [152] A. Alhamoud, P. Xu, F. Englert, A. Reinhardt, P. Scholl, D. Boehnstedt, R. Steinmetz, Extracting human behavior patterns from appliance-level power consumption data, in: European Conference on Wireless Sensor Networks, Springer, 2015, pp. 52-67. [153] M. Baptista, A. Fang, H. Prendinger, R. Prada, Y. Yamaguchi, Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques, in: AAAI, 2014, pp. 1164-1170. [154] X.X. Ren, D. Yan, T.Z. Hong, Data mining of space heating system performance in affordable housing, Building and Environment, 89 (2015) 1-13. [155] Z. Yu, F. Haghighat, B.C.M. Fung, E. Morofsky, H. Yoshino, A methodology for identifying and improving occupant behavior in residential buildings, Energy, 36 (11) (2011) 6596-6608. [156] D. Aerts, J. Minnen, I. Glorieux, I. Wouters, F. Descamps, A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison, Building and Environment, 75 (2014) 67-78. [157] P.D. Andersen, A. Iversen, H. Madsen, C. Rode, Dynamic modeling of presence of occupants using inhomogeneous Markov chains, Energy and Buildings, 69 (2014) 213-223. [158] C.F. Reinhart, Lightswitch-2002: a model for manual and automated control of electric lighting and blinds, Sol. Energy, 77 (1) (2004) 15-28. 36
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
[159] J. Liisberg, J.K. Møller, H. Bloem, J. Cipriano, G. Mor, H. Madsen, Hidden Markov Models for indirect classification of occupant behaviour, Sust. Cities Soc., 27 (2016) 83-98. [160] O.G. Santin, Behavioural Patterns and User Profiles related to energy consumption for heating, Energy and Buildings, 43 (10) (2011) 2662-2672. [161] F. McLoughlin, A. Duffy, M. Conlon, Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study, Energy and Buildings, 48 (2012) 240-248. [162] T. Labeodan, K. Aduda, G. Boxem, W. Zeiler, On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction - A survey, Renew. Sust. Energ. Rev., 50 (2015) 1405-1414. [163] H. Zhou, L.F. Qiao, Y. Jiang, H.J. Sun, Q.Y. Chen, Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach, Energy and Buildings, 111 (2016) 233-241. [164] I.P.o.C. Change, Climate Change 2007-Mitigation of Climate Change: Working Group III Contribution to the Fourth Assessment Report of the IPCC, Cambridge University Press, 2007. [165] B.E. Metz, Climate change 2001: mitigation: contribution of Working Group III to the third assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2001. [166] W. Kleiminger, F. Mattern, S. Santini, Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches, Energy and Buildings, 85 (2014) 493-505. [167] T. Hong, S.C. Taylor-Lange, S. D'Oca, D. Yan, S.P. Corgnati, Advances in research and applications of energy-related occupant behavior in buildings, Energy and Buildings, 116 (2016) 694-702. [168] E. Azar, C.C. Menassa, A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks, Energy Policy, 67 (2014) 459-472. [169] A.R. Carrico, M. Riemer, Motivating energy conservation in the workplace: An evaluation of the use of group-level feedback and peer education, J. Environ. Psychol., 31 (1) (2011) 1-13. [170] R.A. Winett, J.H. Kagel, Effects of information presentation format on resource use in field studies, J. Consum. Res., 11 (2) (1984) 655-667. [171] M.L. Dennis, E.J. Soderstrom, W.S. Koncinski, B. Cavanaugh, Effective dissemination of energy-related information: Applying social psychology and evaluation research, American Psychologist, 45 (10) (1990) 1109. [172] O.G. Santin, Occupant behaviour in energy efficient dwellings: evidence of a rebound effect, J. Hous. Built Environ., 28 (2) (2013) 311-327. [173] J.D. Khazzoom, Economic implications of mandated efficiency in standards for household appliances, The energy journal, 1 (4) (1980) 21-40. [174] E.G. Hertwich, Consumption and the rebound effect: An industrial ecology perspective, Journal of industrial ecology, 9 (1‐ 2) (2005) 85-98. [175] P.H. Berkhout, J.C. Muskens, J.W. Velthuijsen, Defining the rebound effect, Energy Policy, 28 (6) (2000) 425-432. [176] V. Tiefenbeck, T. Staake, K. Roth, O. Sachs, For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign, Energy Policy, 57 (2013) 160-171. [177] S.Z. Attari, M.L. DeKay, C.I. Davidson, W.B. De Bruin, Public perceptions of energy consumption and savings, Proceedings of the National Academy of sciences, 107 (37) (2010) 16054-16059. [178] C.J. Midden, F.G. Kaiser, L. Teddy McCalley, Technology's four roles in understanding individuals' conservation of natural resources, Journal of Social Issues, 63 (1) (2007) 155-174. [179] K. Gillingham, M.J. Kotchen, D.S. Rapson, G. Wagner, Energy policy: The rebound effect is overplayed, Nature, 493 (7433) (2013) 475-476. [180] L.A. Greening, D.L. Greene, C. Difiglio, Energy efficiency and consumption—the rebound effect—a survey, Energy Policy, 28 (6) (2000) 389-401. [181] Z. O'Neill, F.X. Niu, Uncertainty and sensitivity analysis of spatio-temporal occupant behaviors on residential building energy usage utilizing Karhunen-Loeve expansion, Building and Environment, 115 (2017) 157-172. [182] G. Buttitta, W. Turner, D. Finn, Clustering of household occupancy profiles for archetype building models, in: R. Howlett, A. Capozzoli, V. Serra (Eds.) 8th International Conference on Sustainability in Energy and Buildings, Seb-16, Vol. 111, Elsevier Science Bv, Amsterdam, 2017, pp. 171-180. [183] Y.S. Chiou, K.M. Carley, C.I. Davidson, M.P. Johnson, A high spatial resolution residential 37
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
energy model based on American Time Use Survey data and the bootstrap sampling method, Energy and Buildings, 43 (12) (2011) 3528-3538. [184] E.I.A. (EIA), Consumption surveys: Residential Energy Consumption Survey (RECS) and Commercial Buildings Energy Consumption Survey (CBECS), in, https://www.eia.gov/consumption/, 2015. [185] X. Bai, S. Dhakal, J.K. Steinberger, H. Weisz, Drivers of urban energy use and main policy leverages, in: Energizing Sustainable Cities, Routledge, Taylor & Francis Group, 2013. [186] T. Lin, Y. Yu, X. Bai, L. Feng, J. Wang, Greenhouse gas emissions accounting of urban residential consumption: a household survey based approach, PloS one, 8 (2) (2013) e55642. [187] W. Abrahamse, L. Steg, C. Vlek, T. Rothengatter, A review of intervention studies aimed at household energy conservation, J. Environ. Psychol., 25 (3) (2005) 273-291.
38
Rethinking the role of occupant behavior in building energy performance: A review Yan Zhang , Xuemei Bai , Franklin P. Mills , John C.V. Pezzey PII: DOI: Reference:
S0378-7788(18)30757-6 10.1016/j.enbuild.2018.05.017 ENB 8558
To appear in:
Energy & Buildings
Received date: Revised date: Accepted date:
7 March 2018 2 May 2018 7 May 2018
Please cite this article as: Yan Zhang , Xuemei Bai , Franklin P. Mills , John C.V. Pezzey , Rethinking the role of occupant behavior in building energy performance: A review, Energy & Buildings (2018), doi: 10.1016/j.enbuild.2018.05.017
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Highlights: A basic and comprehensive picture of research on occupant behavior and building energy performance is presented by systematic review of the literature.
Four critical research topics are identified. Energy-saving potential of occupant behavior is discussed and estimated to be in the range of 10%-25% for residential buildings and 5%-30% for commercial buildings.
Four existing research gaps are identified and discussed.
AC
CE
PT
ED
M
AN US
CR IP T
1
ACCEPTED MANUSCRIPT
Rethinking the role of occupant behavior in building energy performance: A review Yan Zhang a, Xuemei Bai a, *, Franklin P. Mills a, John C. V. Pezzey a
* Corresponding authors: Prof. Xuemei Bai Tel: +61 2 6125 7825
AN US
Email address:
CR IP T
a. Fenner School of Environment & Society, Australian National University, Linnaeus Way, Canberra, ACT 2601, Australia
[email protected] Postal address:
M
Frank Fenner Building #141, Linnaeus Way, ANU, Canberra ACT 2601,
AC
CE
PT
ED
Australia
2
ACCEPTED MANUSCRIPT
Abstract:
ED
Declaration of interest:
M
AN US
CR IP T
Technology alone will not achieve building energy conservation goals, and humans and their energy-related behavior in buildings must be included in energy performance efforts. Despite many studies revolving around human behavior and building energy performance, the understanding of occupant behavior and its role in building energy performance remains vague, confusing and inconsistent. We attempt to rethink occupant behavior and its role in building energy performance by means of review. Relevant articles have been collected from Web of Science and the basic picture of research has been presented. In-depth review focuses on four critical research topics: a) the current understanding of occupant behavior, with particular focus on window opening behavior, lighting control behavior, and space heating/cooling behavior; b) methods and techniques for collecting data on behavior and building energy performance; c) quantitative modeling of occupant behavior and building energy performance; and d) evaluation of energy saving potentials of occupant behavior based on existing literature. We estimate the energy-saving potential of occupant behavior to be in the range of 10%-25% for residential buildings and 5%-30% for commercial buildings, based on findings of existing research. From our analyses, we identify four existing research gaps, namely the needs for understanding occupant behavior in a systematic framework; for stronger empirical evidence beyond individual buildings and at a larger city scale; for linking occupant behavior to socio-economic and policy variables; and for evaluating the role of occupant behavior in the effectiveness of building energy efficiency policy.
PT
This work was partly funded by the China Scholarship Council (CSC) under the Grant No. 201506030015.
AC
CE
Keywords: Occupant behavior; Buildings; Building energy performance; Building energy efficiency
3
ACCEPTED MANUSCRIPT
1. Introduction
AN US
CR IP T
Energy and climate issues have gained extensive attention of both the public and academia as human and natural systems are being exposed to more risks caused by ever-increasing greenhouse gas (GHG) emissions from primary energy consumption [1, 2], including increasing severity and frequency of extreme weather across the globe such as heat and cold waves, flash floods and tropical cyclones [3, 4]. The building sector accounted for nearly 40% of total global energy use and 33% of total GHG emissions in 2016 [5], which would be even greater if the embodied energy use and related emissions are taken into account [6-8] for the whole life cycle of buildings. Energy consumption from buildings and activities in buildings is projected to increase by an average of 1.5% per year for the period of 2012-2040 under a business as usual scenario [9], and may double or even triple by 2050 from the level of 2010 [10]. Although building energy demand in developing countries and global building energy use have been trending upward [10], the massive, cost-effective opportunities for saving energy in the building sector could largely curb or even reverse growth in global energy demand within and beyond buildings [8, 11-13]. Thus, the building energy sector represents a significant opportunity for both accelerating the energy transition and ensuring a low-carbon future worldwide [10, 14, 15].
AC
CE
PT
ED
M
Technological innovations and improvements have long been at the center of policies and approaches to improving building energy performance and reducing building energy use. For example, the International Energy Agency [16] projected great energy savings (approximately 6 exajoules in total) could be achieved from building envelopes in 2050 under the 2ºC maximum warming scenario from a wide range of building envelope technologies, such as advanced facades, highly insulated windows, automated dynamic shading and glazing, and energy-efficient building materials. Moreover, a report [17] for the U.S. Department of Energy developed a research and development roadmap for heating, ventilation, and air conditioning (HVAC) technologies, identifying high-priority initiatives for high efficiency HVAC technologies like advanced direct-current-powered HVAC system, techniques improving heat pump performance at low-ambient temperatures, and electrochemical compression systems. Other building energy efficiency technologies, such as energy efficient appliances [18] and building automation and control systems [19, 20], have also been widely discussed. However, neither significant improvement of per capita final energy use from buildings (Figure 1) nor the expected energy use reductions [21] has been seen as a result of these technological solutions and efforts. This is partly due to low adoption rates which have been limited to some extent by the high cost of adopting new energy efficiency technologies [22, 23]. Moreover, some recent surveys in the UK [21] and Finland [24] have revealed that over 40% of the population are not interested in new technologies and are thus reluctant to purchase and adopt an energy-efficient technology or service. Researchers also have found that even for almost identical buildings, there can be huge disparities among their annual energy consumptions [25-27]. Furthermore, there are emerging novel 4
ACCEPTED MANUSCRIPT
research agendas that aim to investigate and highlight occupant behavior as a critical influence on building energy consumption that can maximize building energy efficiency to the same extent that technological solutions can [28-30].
AC
CE
PT
ED
M
AN US
CR IP T
More and more researchers [31-36] have come to realize the importance of occupant behavior in building energy performance, identifying occupant behavior as the main cause of the ineffectiveness of technology solutions and the unrealized savings of energy consumption in buildings. Better understanding of human activities, urban energy system and climate responses in cities, as well as their interactions, is urgently needed to reduce carbon emissions and urban risks from global warming [37, 38]. Additionally, for developing countries, the building energy consumption is likely to increase with the rapid urbanization and economic development, during which occupants‘ awareness and behavior are critical to curbing the growth of energy use in buildings [39, 40]. Compared to technological approaches, behavioral programs and interventions can be highly cost-effective [41] and are more likely to be adopted by a wider range of building users. In terms of the implementation of building energy policies, some policymakers and officials in the building sector also have come to realize the ineluctable role of occupant behavior in the effectiveness of relevant strategies [22, 42]. To date, the studies looking at occupant behavior and building energy performance mostly investigate into one or two specific kinds of energy-related behavior in one building or area. In addition, although researchers from different disciplines have delved into occupant behavior and energy use in buildings, often without common language, the findings usually lack comparability and are difficult to generalize beyond the specific context for each study [43]. Thus, it is difficult to grasp a holistic understanding of occupant behavior and its impact on building energy performance. Specifically, these questions remain unanswered: a) what is the state-of-the-art understanding of occupant behavior in relation to energy use in buildings? b) how has the research evolved and what are the hot topics and research gaps? c) what kind of methodologies have been used for research? d) what is the range of energy saving potential of occupant behavior? This paper explores these questions through a systematic review of literature, as shown in Figure 2. The following section presents the research landscape of occupant behavior and building energy performance, followed by a bibliometric analysis of literature in Section 3, aiming at identifying research trends and key topics. Sections 4-7 explore the key foci for recent literature, as revealed by the bibliometric analysis. Section 8 discusses major research gaps, and Section 9 presents the conclusion.
5
CR IP T
ACCEPTED MANUSCRIPT
Figure 1. Annual per capita final energy use of residential and commercial buildings for eleven regions in
Figure 2 Research framework
CE
PT
ED
M
AN US
1990 and 2010 （Source: IPCC, 2014）
AC
2. Research landscape of occupant behavior and building energy performance Research on occupant behavior and building energy performance can be divided into five clusters, based on research purpose. One is to understand the environmental psychology of occupants by identifying and assessing the key factors that influence the energy-related behavior of building occupants [44-47]. In this context, occupant behavior is viewed as a bridge or link between key influential factors and typical energy consumption patterns. This research cluster identifies and studies the factors that have the greatest influence on behavior across a range of scenarios. The second cluster is about developing and testing intervention strategies and methods to reduce energy use. Information intervention appears to be an effective 6
ACCEPTED MANUSCRIPT
strategy that has been widely studied and applied across the world [48-53]. For example, Magali et al. (2013) conducted a meta-analysis of information-based energy conservation experiments, analyzing 56 published trials and 524,479 study subjects from 1975 to 2012 [54]. They found that individuals in the experiments reduced electricity consumption averagely by 7.4% and that individualized feedback via audits and consulting was the most effective information strategy.
M
AN US
CR IP T
The third cluster is about characterizing occupant attitudes and assessing the effectiveness of energy policies. A key finding is the need to integrate occupant behavior into the current policy framework for building energy efficiency. Although installation of more energy efficient equipment is a common policy, increasing occupants‘ awareness of energy-related behavior and thereby change their behavior is more cost-effective [22, 33]. Some research [55, 56] has highlighted the importance of occupant behavior in building energy performance through surveys that identify the energy saving potential from occupant behavior for the building sector. For instance, Hu et al. (2017) [57] conducted an online survey on energy consumption and behavior in urban Chinese households. They collected data on occupants and buildings, energy consuming devices and occupant usage of them, overall energy consumption, and occupants‘ satisfaction with the indoor environment, and occupants‘ attitudes toward energy-saving policies. Based on their analysis of relevant survey data, they concluded that China‘s urban households‘ behavior is decentralized, varying and with low energy consumption.
CE
PT
ED
The fourth cluster focuses on evaluating the effectiveness of energy efficient technology and building design [58-63]. This cluster of research uses occupant behavior models as an input when predicting the expected energy performance of building designs [64-67]. Mejri et al. (2011) [67] proposed a method to assess energy performance of occupied buildings using model identification techniques in order to improve the prediction of building energy performance. Ioannou and Itard (2015) [68] investigated energy simulation tools and analyzed building and behavioral parameters influencing the simulation results of residential heating energy.
AC
The last cluster has been developing predictive, quantitative models of occupant energy-related behavior. The significant gaps that have been identified between expected and actual energy consumption in many experiments [69-77] suggest significant improvements are needed to understand the impact of occupant behavior on energy use. 3. Bibliometric analysis: Research trends and hot topics Following the research landscape presented above, this section aims to identify research trends and hot topics by means of bibiometric analysis. Bibliometrics is a powerful quantitative tool to explore knowledge networks based on published literature and has been widely used for studying the structure and development of a research field [78, 79], including research of energy and climate change [80, 81]. 7
ACCEPTED MANUSCRIPT
CR IP T
This paper therefore applied bibliometrics to research of occupant behavior in buildings. A broad search strategy for academic literature published in English was adopted, using the online database Web of Science TM Core Collection – Citation Indexes (SCI-EXPANDED & SSCI) with the document type limited to research articles and reviews. The terms used for searching by topic were ―occupant* or human or user*‖ and ―behavio*‖ and ―energy‖ and ―building* or hous* or office*‖. The search was first carried out in October 2016 and updated twice in April and August 2017. There were 1321 entries obtained in total. Then 206 were excluded based on relevance as they are not about occupant behavior and building energy performance, and as a result, a collection of 1115 publications were reviewed in this study.
AC
CE
PT
ED
M
AN US
In general, there is a growing trend in the yearly output of articles from 1978 to 2016 (Figure 3), which can be divided into two phases. The first is during 1978~2004, when there were fewer than 10 papers published each year without significant growth in output. The first paper appeared in 1978, authored by Socolow [82] in Princeton. It examined the role of residents and their behavior in energy consumption for space heating, based on a five-year field study of identical townhouses in Twin Rivers, New Jersey. The results of the experiments in the Twin Rivers program confirmed that the residents and their energy-related behavior in houses mattered with observed variation in energy consumption of identical houses with different occupants [82]. In addition to Socolow‘s research, Seligman [83] and Sonderegger et al. [84] also highlighted the impact of occupant behavior on energy performance of residential buildings. Both of their studies were sourced from the Twin Rivers program, as used by Socolow [82]. After carrying out two attitudinal surveys from the perspective of social psychology, Seligman [83] concluded that the resident can play an important role in energy conservation that complements engineering solutions. Sonderegger et al. [84] found that about 33% of the variation in gas consumption of 205 identical townhouses could be caused by occupant-related consumption patterns, of which persistent occupant-related patterns (‗lifestyle‘) explained 18% of the variation and non-persistent patterns (‗change‘) 15% of it. Furthermore, some researchers turned their eyes to occupant behavior in commercial buildings. For instance, Norford et al. [72] made an effort to identify the reasons for the wide discrepancy between actual and predicted energy use in a ‗low-energy‘ office building, indicating what really mattered was how the building was used by its occupants, rather than how the building was constructed. The second phase is from 2005 onwards, with output of papers growing rapidly at around 20% per year, and now exceeding 200 publications in 2016. Recent growth may be partly because the role of occupant behavior in building energy performance and energy conservation has been highlighted by some influential reports like the IPCC Fifth Assessment Report [10] and the IIASA Global Energy Assessment [39]. Despite the incomplete retrieval of relevant publications in 2017, it is worth noting that a four-year project, IEA-EBC Annex 66 [85], just ended in 2017, centered on the definition and simulation of occupant behavior in buildings and produced over 100 journal 8
ACCEPTED MANUSCRIPT
AN US
CR IP T
publications during its term.
AC
CE
PT
ED
M
Figure 3 Publications from 1978 to 2016
9
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Figure 4 shows the distribution of publications by country of the author (not limited to the first author of a publication, including all authors), source journal, institution and author. In terms of the publications by country, the USA has a total of 238 papers in this research field, accounting for about 21.3% of the whole related literature, followed by the UK with 228 publications (20.4%). The average citations within the Web of Science of articles from the USA and the UK are 68.27 and 30.44, respectively. It is obvious that the USA occupies a leading position in research of occupant behavior and building energy performance, as shown in Figure 4(a). When it comes to the journals in which the papers were published, most of the studies are concentrated in journals whose 2016 Impact Factors ranging from 3 to 6, among which the journal Energy and Buildings (IF: 4.067) takes the first place (294, 26.4%)
10
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
by the number of articles. The institution with the largest number of publications is Tsinghua University (34, 3%), followed by Delft University of Technology (27, 2.4%) in Netherlands, Polytechnic University of Turin (27, 2.4%) in Italy and University of California, Berkeley (27, 2.4%) in the USA. Moreover, there are about 2875 authors involved in this field of research, and the number of authors who published at least 5 papers in our reviewed literature is 60 (2.1%). Of those 2875 authors, 2419 (84.1%) only have one publication and 290 (10.1%) have two publications, indicating that a limited group of researchers (166, 5.8%) have been focusing on this field. The top 12 authors having published at least 10 papers are as shown in Figure 4(b). The top two researchers, Hong TZ and Yan D, appears to be closely collaborating.
Figure 4 Distribution of publications
11
ACCEPTED MANUSCRIPT
CR IP T
Among those articles, there are 81 papers having been cited at least 81 times (i.e. h-index = 81). One of the most highly cited articles was published in Energy and Buildings in 2009, with a local citation score of 93 (i.e. 93 times being cited by the papers in our collection of literature) and 183 citations within the Web of Science. This article discussed the effect of user behavior on building performance from the perspective of the building design phase to perfect the building performance simulation, suggesting that more details should be integrated into the user behavior assessment for specific buildings and the actual occupants and their peculiarities need to be considered during the building design phase for better outcomes of building energy simulation [86]. In fact, recently a large body of research (236 papers, 21.2%) has focused on occupant behavior and its effect on building energy simulation [87-90], while some studies (78 papers, 7.0%) concentrated on those factors influencing occupant behavior [44, 46, 91, 92].
CE
ED
Understanding of occupant behavior, especially specific behaviors affecting indoor thermal comfort, such as HVAC-related behavior, and lighting- and window-related behavior (relevant keywords: thermal comfort, overheating, natural ventilation, lighting, ventilation, window opening, etc.) (Section 4) ; Research methods and energy data collection (relevant keywords: bottom-up approach, framework, monitoring, energy management, thermostats, etc.) (Section 5) ; Quantitative modeling of occupant behavior and building energy performance (relevant keywords: simulation, building energy simulation, model, optimization, energy modeling, etc.) (Section 6) ; Energy saving potential and behavioral strategies (relevant keywords: energy saving, feedback, information, intervention, etc.) (Section 7).
PT
M
AN US
By going through the keywords of the literature, hot topics can be identified with clustering techniques, as shown in Figure 5. The threshold for a keyword included in analysis is a frequency of at least 10. Each circle with a label represents a keyword and the size of the circle is determined by the frequency of the corresponding keyword. Lines between circles represent the links of keywords. The closer two keywords are located to each other, the more frequently they co-occur. Different colors refer to different clusters of keywords, and each cluster represents a hot topic of research. Therefore, four critical research topics can be identified as follows:
AC
Moreover, Figure 6 displays the research trends of occupant behavior in association with building energy performance, by means of presenting changes and evolution of hot topics over time from 2011 to 2016. All keywords shown in the figure appeared at least 10 times in the collection of literature. An average value of the published years of all papers on one topic is assigned to that topic, and used to determine the color in the figure. Collectively, the shifting color among the hot topics can reveal, to some extent, the shifting research focus. It can be seen that the research hot topics 12
ACCEPTED MANUSCRIPT
ED
M
AN US
CR IP T
shifted from behavioral strategies and psychological analysis to behavioral modeling and building energy simulation, which suggests a shift of research focus from qualitative research to quantitative analysis and modeling. Some specific research topics like indoor thermal comfort-related behavior and feedback strategies have obtained sustained research attention throughout the period. The hot topics identified above will be reviewed and discussed in depth in the following four sections.
AC
CE
PT
Figure 5 Clustering and network visualization of keywords
13
AN US
CR IP T
ACCEPTED MANUSCRIPT
M
Figure 6 Evolution of hot topics over time from 2011 to 2016
4. Understanding of energy-related occupant behavior
AC
CE
PT
ED
A lack of understanding of energy-related occupant behavior (hereafter, occupant behavior) in buildings has long been one significant hindrance to improving building energy performance [93]. Despite the fact that a large number of studies have investigated occupant behavior in the context of building energy, there is no consistent standard definition for the term ‗occupant behavior‘, with different researchers having different interpretations of defining and categorizing occupant behavior. Some studies included investment behavior in energy efficient measures of buildings (e.g. whether to buy an energy efficient appliance; whether to purchase and install solar panels for housing) as part of occupant behavior [22, 48, 94], while some strictly targeted energy-consuming activities when people occupied the buildings [69, 95-97], and some included non-energy-use behavior like putting on clothes and having hot drinks [98]. The International Energy Agency (IEA) in one of their reports attempted to define energy-related occupant behavior as ―observable actions or reactions of a person in response to external or internal stimuli, or respectively actions or reactions of a person to adapt to ambient environmental conditions such as temperatures, indoor air quality and sunlight‖ [99]. One project supported by the IEA Energy in Buildings and Community (EBC) Program for the period of 2013~2017 aimed to establish a standard occupant behavior definition platform to facilitate the quantitative simulation in buildings [85]. 14
ACCEPTED MANUSCRIPT
M
AN US
CR IP T
Considering the complex and stochastic characteristics of occupant behavior, Chen et al. (2015) developed a three level system of definitions of occupant behavior for different research purposes in residential buildings: 1) a simple-level definition with several parameters regarding the schedule of occupancy and domestic appliances for statistical analysis, 2) an intermediate-level definition with additional parameters about the set points of appliances and the operation schedule for case studies, and 3) a complex-level definition with descriptions on the schedule, set points and control rules of three types of occupant behavior, i.e. occupancy, appliance operation, and window/shading operation, for detailed diagnostics/simulations of building energy performance [93]. Gardner and Stern (1996) divided behaviors related to household energy conservation into two categories: efficiency and curtailment behaviors [100]. Efficiency behaviors are one-shot behaviors and involve the purchase of energy-efficient equipment, such as installation of thermal insulation. Curtailment behaviors involve repetitive efforts to reduce energy use, such as lowering thermostat settings. Wood and Newborough (2003) indicated that residential occupant behavior that can be improved covered both simple changes of energy use behavior and more complex, skill-oriented behavior related to interacting with household equipment [101]. Simple changes may include opening/closing a window or door, adjusting blinds, switching on/off lights, changing clothing, etc. More complex, skill-oriented behavior may require that the occupants have a good knowledge of the operation of energy-consuming equipment and how it works in households, like washing machines and stoves that involve complex decisions regarding how to set controls [102].
AC
CE
PT
ED
A substantial portion of the literature examined the occupant behavior in buildings with a particular focus on one or two specific types of behavior. Window opening behavior is one kind of energy-related behavior that has been widely discussed in studies. For example, Cali et al. (2016) analyzed window opening behavior in German households [103], and Jian et al. (2011) similarly focused on window opening behavior in five representative apartments in Beijing, China [104]. In particular, one highly cited paper proposed a theoretical framework to address interactions between occupants and building controls, highlighting the window opening behavior in buildings [46]. Fabi et al. [46, 105, 106] explained that the reason for window opening behavior obtaining so much research attention was that the window opening behavior has a very big impact both on the indoor environment quality and on the energy consumed to sustain the desired indoor environmental quality level. Some studies focused on the dominant factors and driving forces of window adjusting behavior of occupants in buildings [45, 95, 107], and some targeted effects of window opening behavior on energy consumption in buildings [108, 109]. For example, Wang and Greenberg [110] assessed various control strategies on window opening behaviors using the EnergyPlus simulation software. They pointed out the significant role of window opening behavior in occupants‘ indoor comfort and the HVAC system could achieve energy savings of up to 47% with mixed-mode ventilation for summers. Table 1 summarizes five categories of 15
ACCEPTED MANUSCRIPT
key variables influencing window opening behavior for residential and office buildings from the literature, including physiological factors (e.g. age and gender), psychological factors (e.g. perceived illumination and temperature preference), social factors (e.g. smoking behavior and presence at home), contextual factors (e.g. dwelling type, room type and season) and natural environmental factors (e.g. outdoor temperature, outdoor air quality and indoor temperature).
AN US
CR IP T
Apart from the window opening behavior, lighting control behavior also obtained exclusive attention from some researchers [111, 112]. For instance, Bourgeois et al. (2006) investigated the total energy effect of manual and automated lighting control based on a sub-hourly occupancy-based control model [90]. Ochoa et al. (2012) presented the current state of the art in lighting simulation, pointing out the lack of advanced models for representation of certain physical phenomena [113]. In particular, lighting-related behavior and its associated energy use in office buildings have been extensively studied with experiments by many researchers [53, 114-118]. Studies show that outdoor illuminance and occupant behavior are identified as two main factors influencing lighting energy use in buildings [114, 115, 119-121], and lighting behavior of occupants is in turn influenced by occupancy, time of the day and occupants movement within the building [116].
AC
CE
PT
ED
M
Another type of behavior that has been frequently discussed is space heating/cooling behavior. This is largely because the energy use associated with space heating and cooling significantly contributes to the total final energy use of buildings, both residential and commercial (up to 73% in some cases [122], on average 34% and 40% in residential and commercial buildings, respectively [10]). Experimental evidence has corroborated that heating energy use in residential buildings is significantly influenced by heating triggering/set-point temperature [123], which greatly depends on occupant behavior. Majcen et al. (2015) developed a statistical model for the heating prediction gap (i.e. the discrepancies between the normalized theoretical and actual heating consumption) with the example of households in the Netherlands, finding that significant factors affecting heating behavior and associated energy consumption involve occupants‘ perception of heat/cold, occupants‘ perception of dry/humid air, occupancy, time of the day, thermostat setting, ventilation system, and heating type [70].
16
ACCEPTED MANUSCRIPT
Table 1 Factors influencing window opening behavior in buildings (Source: summarized based on references [46] , [45] & [103])
Physiological Factors
Psychological Factors
Social Factors
Contextual Factors
Natural Environmental Factors
Age
Gender
Smoking behavior
Perceived illumination
Preference in terms of temperature
Presence at home
Room orientation
Outdoor quality
Ventilation type
Indoor temperature
Heating system
Indoor relative humidity
Solar radiation
Season
Wind speed
Time of day
CO2 concentrations
Window type
Outdoor temperature
Season
Time of day
Outdoor quality
Night ventilation
Indoor temperature
Indoor relative humidity
Solar radiation
Wind speed
Rain
AN US
Gender
Shared offices
Preference in terms of temperature
AC
CE
PT
Office buildings
Perceived illumination
ED
Age
M
Outdoor temperature
Room type
Residential buildings
Dwelling type
CR IP T
Building Type
5. Research methods and data collection techniques Occupant behavior is complex and stochastic in nature and it is hard to accurately describe or predict in the context of building energy performance. A rather common technical framework of research 17
air
air
AN US
CR IP T
ACCEPTED MANUSCRIPT
(
AC
CE
PT
ED
M
Figure 7) can be identified, which is used in many studies on occupant behavior and building energy performance. The initial step of research is data collection of occupant behavior and other relevant variables, together with modelling of occupant behavior. The choice of data collection approaches should align with the determination on behavioral models. Then the collected data and the chosen behavioral model will be used to analyze occupant behavior and building energy performance. As a result of the analysis, output of the relationship between occupant behavior and building energy performance can be determined. In order to validate the accuracy of developed models, an assessment of the models will be conducted based on the output. Finally, insights and implications of the research results can be explored. Clearly, it is essential to accurately select, capture, and model occupant behavior before further research on occupant behavior and building energy performance. Our analysis of the literature [124] shows data on occupant behavior are generally acquired through two main approaches – one is monitoring equipment installed within buildings (i.e. monitoring methods), and the other is depending on reports from occupants themselves (i.e. self-reports). Typical monitoring methods include ultrasonic and passive infrared detection, CO2 sensors, camera-based technologies, and wireless network based systems [124]. Reports from occupants usually encompass information and concerned details on occupants and their behaviors, and probably involve energy consumption and energy bills of buildings when the objective of research is to examine the effect of specific behavior on building energy consumption. 18
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Jia et al. (2017) summarized some of the state-of-the-art data collection technologies used for the research of occupant behavior [74], as shown in Figure 8. Cameras and passive infrared (PIR) sensors are commonly used for capturing the ground truth such as occupancy and occupant activities. Wireless Sensor Networks (WSN) and weather stations are the most popular avenues to gathering indoor and outdoor environmental data. Meters for electricity and gas are employed to provide information on energy consumption and usage patterns. Some other technologies like radio-frequency identification (RFID), ultra-wideband (UWB), Wi-Fi and customized sensors can be adopted based on specific research purposes.
19
ACCEPTED MANUSCRIPT
Figure 7 Technical framework of research on occupant behavior and building energy performance in literature
M
AN US
CR IP T
existing
Figure 8 State-of-the-art technologies for data collection in occupant behavior studies (adapted from [74])
AC
CE
PT
ED
Both monitoring methods and self-reports from occupants (Figure 7) have pros and cons. Monitoring methods require both the installation of devices, such as sensors and meters, and the involvement of technical experts, which may incur significant costs and take a long period of time. Thus, while it can provide data with finer granularity, it perhaps is more suitable for small scale research. Self-reports from occupants are easier to carry out, using either questionnaires or other survey tools, without large investments of money and experts‘ knowledge. While they may be more suitable for larger scale study, a mismatch may emerge between actual and reported information when self-reports are used for analysis, which may affect the accuracy of the conclusions. In practice, the purposes of research and available resources will determine which approach, or a hybrid of both, is used. 6. Quantitative modeling of occupant behavior and building energy performance Occupant behavior can be represented quantitatively at a certain level, despite its stochastic and complex nature, by means of mathematical models [41]. Occupant behavior can thus be integrated into building energy performance simulation and modeling to capture its impacts on energy and environmental performance of buildings [92]. Table 2 shows four categories of quantitative models found in the literature, using the way of categorization developed by Jia and co-authors [74, 125]. 20
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
It should be noted that such quantitative modeling methodologies often overlap to some degree, and can be combined in many ways for different research purposes. So the four categories presented below – stochastic/ probabilistic modelling, statistical methods, agent-based modeling and data-mining approaches – are not mutually exclusive, but they are commonly used in recent literature [29, 126, 127].
21
CR IP T
ACCEPTED MANUSCRIPT
Table 2. Four categories of quantitative models of occupant behavior (based on Ref. [74] and other references indicated)
Year
Building type
Technology
Variables
Stochastic/ probabilistic modeling
2011
University building
A system comprising 16 sensor nodes that consist of a camera with an adapter board
Record of transitions whenever a person crosses [128] a point.
2011
Office building
A wired sensor network comprising wireless sensor array; Cameras
CO2, temperature, TH, acoustics, lighting and motion detection; occupancy information
[129]
2014
Museum
Not reported
[130]
2015
Commercial
Wireless camera
Mean and variance of durations for occupants (assumed from museum open hours and Hong Kong building energy code) Occupancy data
2012
Not reported
Not reported
[132]
2014
Residential
Web-based survey
State of occupant behaviors with certain probability Individual attributes; household attribute; energy use per service unit
2017
Lab building
2017
Residential
Electricity meters
2014
Dormitory building
The program @RISK; electricity survey
M
ED
PT
CE AC
AN US
Methodology
A multi-layered supervised feedforward Artificial Neural Network (ANN);
22
Reference
[131]
[133]
building occupancy; presence/absence [134] (occupied status) of people in a building zone; number of occupants within the building zone; air change rates; number of people, appliances, lighting Electricity use; active occupancy; daily activity; [135] number and time of daily use of domestic appliances Occupancy; electricity use [136]
Questionnaire for occupant related parameters; temperature sensors for ambient monitor
2009
Office building
Occupancy sensors; digital photography; Indoor/outdoor environment data; occupant weather station presence intervals; positions and shades
[138]
2013
Residential
Ambient sensors, Hobo U9 sensors
[139]
2012 2010
Residential Office building
2015
Office building
2015
Office building
Indoor/outdoor environment parameters, window/door state, moment of the day, season, presence Heat sensors, power meters Indoor temperature; power usage Web-based survey Previous activity, working day, current activity, probability for next activity Self-developed door/window open status Six variables: Indoor/outdoor temperature and recording device humidity, wind speed, indoor CO2 concentration Wireless ceiling-mounted motion Occupancy state (duration) sensors
2012
Commercial
Not reported
Measured data: actual temperatures, zone schedules, occupant preferences, and occupant behaviors
[144]
2014
Residential
ABM built by REPAST Simphony
2 classes of agents, which include their own attributes
[145]
2014
Commercial
MATLAB for ABM; EnergyPlus for energy simulation
[146]
2015
Office building
No measured variables; 6 common behaviors that related to thermal comfort; all the input values are based on assumptions Local/global temperature; humidity; air velocity; heater use; window use
AC
Clothing and activity level, thermal sensation and preference, adaptive opportunities exercised; indoor temperature; local climate
ED
M
AN US
Office building
CE
Agent-based modeling
2008
PT
Statistical methods
CR IP T
ACCEPTED MANUSCRIPT
Online surveys; data loggers, power meters
23
[137]
[140] [141] [142]
[143]
[147]
[148]
Office building
Reasonable assumptions (of occupant Three categories of occupants; energy use rate; categories, occupancy related parameters factors that cause behavior change and occupants' interactions) Survey Occupancy schedule; simulated sensor data from other research Plugwise meter, pedometer Electricity consumption of office appliances, occupant behavior Not reported 10-min occupancy interval data
2010
Commercial
2014
Office building
2015 2015
Residential
Appliance-level power sensors
Power consumption
[152]
2014
Residential
Survey documented by occupants
[153]
2015
Residential
Temperature sensors, gas meters
2011
Residential
Field measurement (meters and sensors) and surveys
Self-reported activities on a minute by minute basis Indoor temperatures, gas consumption, time of furnace on/off state End-use loads (electricity, gas, kerosene); indoor temperature; lifestyle and related information of occupants
AN US
Commercial
AC
CE
PT
ED
M
Data-mining approaches
2012
CR IP T
ACCEPTED MANUSCRIPT
24
[149] [150] [151]
[154] [155]
ACCEPTED MANUSCRIPT
ED
M
AN US
CR IP T
a) Stochastic/probabilistic modeling. Stochastic/probabilistic models capture and describe the probability that specific behavior occurs based on historical or statistical data [156]. Generally, there are three types of stochastic/probabilistic occupant behavior models commonly used: Markov chain models [157], Bernoulli process [92], and survival analysis [158]. These models have been widely used to represent both occupancy and the actions occupants take to control their indoor environment. Markov chain models are a commonly-used type of probabilistic model. A Markov chain is a time series in which all states of the system can be directly observed and the future state depends only on the present state, independent of all past states. A Hidden Markov Model (HMM) assumes the possible states of a system are linked in a Markov chain but the states of the system are hidden from direct observation; instead, each system state is associated via a probability distribution with a set of observable parameters. Many researchers have used Markov chain models to describe occupancy status and behavior patterns. For example, Liisberg et al. (2016) [159] utilized HMMs to characterize occupant behavior based on indirect observations. Their investigation of average probability profiles as a function of time of day identified four distinct patterns of occupant behavior. Then based on the time of day dependence observed in their initial results, which used homogeneous HMMs, they developed time dependent HMMs (inhomogeneous HMMs) with improved outcomes of prediction. Markov chain models and HMMs are both stochastic processes. Bernoulli processes can be adopted when the probability of an event/state is not dependent on the previous state (i.e., memoryless), enabling the building energy modeling at the whole-building level [92]. Survival analysis is generally utilized to estimate the time duration of an event/state before a change happens, and can be used to estimate how long a building is likely to remain unchanged by occupants [92]. Due to the randomness and variability of occupant behavior, stochastic models are better in terms of validity and applicability for describing the actual interaction between occupants and building systems than deterministic models [105].
AC
CE
PT
b) Statistical methods. Statistical methods are generally carried out by building numerical relationship between occupant behavior and indoor/outdoor environment conditions, electricity usage or time periods, with the results being expressed by occupancy state or the probability of studied behavior [125]. Statistical analysis can be conducted to identify the patterns of behavior in buildings [160, 161]. One typical example is a 2008 study [137] in which logistic regression was utilized to investigate the effect of thermal stimuli on different types of behavior, such as opening/closing windows, blinds and doors. The researchers found that indoor temperature was a better predictor than outdoor temperature for these occupant behaviors. Statistical methods can better capture and describe occupant behavior in reality, but they require a larger sample size and data input of many variables, which could lead to a higher cost of field studies. c) Agent-based modeling. Agent-based modeling (ABM) uses multi-agent systems, comprised of autonomous agents, to simulate agents‘ interactions with each other and their external environments under specified rules [162]. The rules are critical to the simulation as they define how agents interact with each other and their environment. An example of the application of ABM to occupant-building interaction is Lee and Malkawi‘s (2014) simulation of multiple occupant behaviors in a commercial building [146]. They examined five specific behaviors: adjust clothing level, adjust activity level, window use, blind use, and space heater/personal fan use, as well as studying 25
ACCEPTED MANUSCRIPT how an agent adapts to the dynamic thermal changes in its space to optimize both comfort and energy savings. ABM has unique advantages in modeling occupant behavior as it introduces uncertainties like those in the real world and can describe the dynamics of human-building systems. Moreover, this approach allows the integration of models for both occupant behavior and building energy performance, which could be used in a combined interactive simulation of behavior and energy in buildings. Despite some improvements in the methodology of ABM, more comprehensive and holistic models are needed for further research in future.
ED
M
AN US
CR IP T
d) Data-mining approaches. Data-mining has been used in many recent studies on occupant behavior [151, 154, 163]. They require large databases and huge data storage. The application of data-mining to modeling occupant behavior follows its increasing use in studies of building energy performance as large volumes of data on building energy use and the energy consumed by specific appliances in buildings have become available. Zhao et al. (2014) developed an "indirect" practical data mining approach using office appliance power consumption data as a proxy for occupants‘ "passive" behavior [150]. They found that the average percentage of correctly classified individual behavior instances was 90.29% and their experimental result showed a fairly consistent group occupancy schedule, while capturing the diversity of individual behavior in using office appliances. While a high level of accuracy of behavior pattern prediction can be achieved, the application of this category of methods has been limited to occupancy and appliance use in residential buildings, possibly as a result of insufficient data and restricted access to other behavior and energy use data. For most studies to date, only household electricity use data has been employed for data-mining analysis of occupant behavior patterns. Data mining approaches are intended to overcome the shortcomings of the aforementioned traditional methods, particularly when dealing with big data streams, by offering reliable models of occupant behavior with the potential for rapid analysis and high replication [41, 150, 151].
PT
7. Energy-saving potential of occupant behavior in building energy performance
AC
CE
The critical role of occupant behavior in potential energy savings for the building sector has been recognized by many studies, and is supported by increased evidence presented by a series of IPCC reports [10, 164, 165]. According to the experiments and surveys in literature, behavioral change can reduce energy use by 6% to 25% [21, 101, 166, 167] for residential buildings, and 5% to 30% [167, 168] for commercial buildings. Some research [169] in the workplace indicated that group-level feedback and peer education could lead to a reduction of 8% and 4% in energy use, respectively. Ouyang and Hokao (2009) [48] and Winett et al. (1984) [170] both reported that the energy-saving potential by improving occupants‘ behavior in domestic life through energy-saving education can be more than 10% household electricity use. In the research of Ouyang and Hokao, a series of surveys about monthly electricity use, energy-saving education and household lifestyle were carried out in 124 households in three typical residential buildings in Hangzhou city of China. They examined the relationship between electricity consumption and household lifestyle, confirming that efforts to reduce residential energy use should be shifted from technological measures to improving occupants' behavior in ordinary domestic life. Wood and Newborough (2003) [101] experimented with electronic feedback via smart meters and displays, 26
ACCEPTED MANUSCRIPT
CR IP T
and found that 14 out of 31 households achieved energy savings of greater than 10% and six of these achieved savings of greater than 20%. The average reduction for households was 15%, whereas those given antecedent information (specifically referring to information about practical ways of reducing energy use in the form of pamphlets posted through the door, TV programs or the internet) alone reduced their electricity consumption by only 3% on average. Dennis et al. (1990) indicated that 60% reduction in unnecessary lighting use could be achieved by putting signs near light switches [171]. Table 3 summarizes the energy-saving potential of behavioral change in buildings. For example, it is possible to save 10-15% of the energy consumed for drying clothes if occupants use their clothes dryer at full load rather than at one-third to half load, and even save 100% of that if not using a clothes dryer (i.e. air drying inside when there is no space heating requirement, or outside) [10]. Table 3. Energy-saving potential of behavioral change in buildings
(Note: Proportions of energy consumption by end-use in residential and commercial buildings are based on Ref. [10])
Heating
32%
Hot water
24%
Cooling
2%
Cooking
29%
Lighting
4%
Refrigerators Clothes washers Clothes dryers
-
PT
Total for residential
9%
ED
Dishwashers
Total for commercial
-
Proportion in commercial
Potential energy savings from behavioral change
AN US
Proportion in residential
33%
10%-30%[10]; 51%[96]
12%
50%[10]
7%
50%-67%[10]
-
50%[10]
16%
60%[171]; 70%[10]
M
Energy use in buildings
-
30%[10]; 50%[10] 75%[10] 60%-85%[10] 10%-15%[10]; 10-100%[10]
-
6%-25%[101] [21] [166] [167]
-
5%-30%[167, 168]
AC
CE
It has been observed that some people tend to use energy-consuming devices in buildings more often and for a longer period of time when they learn those devices are energy efficient, which is referred to as the rebound effect and may hinder the realization of behavioral energy-saving potential [172, 173]. The rebound effect can result from the intensification of an activity by occupants. For example, the adoption of energy-efficient appliances does not necessarily result in a reduction of overall energy consumption if people use these appliances more often [174, 175]. Moreover, a behavioral energy conservation campaign conducted by Tiefenbeck et al. (2013) [176] in 154 apartments showed that residents who received weekly feedback on their water consumption lowered their water use (6.0% on average), but at the same time increased their electricity consumption by 5.6% compared with control subjects. This is probably caused by a warm glow effect of ―already doing something‖ among occupants, when they feel satisfied that they have done their part of water efficient actions and then tend to consume more energy in other places with heedless behaviors [177]. Midden et al. (2007) [178] illustrated that technology and behavior were 27
ACCEPTED MANUSCRIPT
CR IP T
closely interwoven in many respects, and indicated various ways in which technological and behavioral factors could be integrated in interactive approaches to effectively promote building energy conservation. Therefore, a more comprehensive framework of intervention programs and strategies targeted on occupant behavior should take the rebound effect into consideration to completely fulfil the energy-saving potential of behavioral change. Meanwhile, some researchers [179, 180] indicated that the rebound effect does exist in reality and should be considered in strategic energy planning, but in some way it is overplayed and can be diluted by other policy options aiming to reduce building energy use, such as stricter building energy codes. 8. Research gaps
Based on our review of the literature on occupant behavior and building energy performance, the following research gaps are identified to further improve building energy performance and reduce building energy consumption.
AN US
(1) Occupant behavior needs to be understood within a systematic framework of research.
AC
CE
PT
ED
M
To date, research on occupant behavior seems to have focused on several specific behaviors, such as window opening behavior, lighting control behavior and space heating/cooling, with few studies including all relevant kinds of occupant behavior into the scope of research. This means there is still room to improve our understanding of occupant behavior with regard to energy use in buildings. A clearer and consistent definition of occupant behavior that is shared by researchers from different disciplines is needed. There are some examples [181] where occupant behavior is narrowed down or treated as equal to occupancy, which is in fact only one element of occupant behavior, as indicated by Jia et al. [74]. Some types of energy-related activities such as television and computer uses in buildings are also relatively rarely examined in studies. Meanwhile, some studies estimated the energy-saving potential from behavioral change, but often focusing on one or two particular types of energy-related behavior, making it difficult to compare results and findings across case studies. This might lead to an underestimation of the overall impact of occupant behavior on building energy performance. Additionally, a systematic framework will enable researchers to model and capture the interaction between occupants and building energy systems from different perspectives with a unified evaluation system. Thus, a more systematic framework of research to define the boundaries of occupant behavior, with all critical elements and links included and clearly described, is urgently needed. (2) Empirical evidence and data at a larger city scale are needed. A significant shortcoming of occupant behavior research is that there are few data on behavior profiles and energy use in real-world buildings. In order to improve outcomes of simulation models, sufficient empirical data are needed to adjust and validate the modeling techniques. Some researchers adopted the data from national time use surveys as a proxy for actual behavior profiles [182, 183]. Empirical evidence and data are critical to occupant behavior and building energy performance 28
ACCEPTED MANUSCRIPT
CR IP T
research and more field surveys should be conducted to enhance existing studies as well as facilitate further analysis in this field. The US national surveys (RECS & CBECS) [184] on energy consumption of residential and commercial buildings have explored and collected data of building characteristics, ownership and operating frequency of energy-consuming equipment and appliances, as well as occupancy and energy bills, missing occupants‘ reaction to indoor discomforts, which is a critical element in occupant behavior research. Rapid urbanization and associated lifestyle change in developing countries like China mean more energy could be consumed in residential buildings of urban areas [185, 186]. Understanding building energy performance beyond individual buildings and at a larger city scale is important for city scale policy making and implementation. However, based on our dataset of literature, little research explores occupant behavior in association with building energy performance at such scale. Furthermore, a comparison with respect to occupant behavior patterns and building energy performances between different cities could add another layer of important insight, but no such work has been seen yet.
AN US
(3) Occupant behavior needs to be understood in the context of socio-economic status and energy policy.
AC
CE
PT
ED
M
Much of the literature on simulation and modeling of occupant behavior puts almost exclusive focus on connecting occupant behavior with natural environmental factors, building characteristics and personal profiles, and largely leaving out the potential impact of socio-economic status, which can be a critical influencing factor and particularly important in large scale empirical studies. The socio-economic context would supplement existing understanding of occupant behavior and could inform better strategies for encouraging more energy efficient behavior. Allcott and Mullainathan (2010) [22] have demonstrated the critical role of behavior in energy policy; equally, it is crucial for policymakers to incorporate behavioral elements in the policy framework of building energy efficiency, just as this paper has indicated. More research focus should be put on incorporating behavioral factors into energy policy-making. For example, some intervention strategies such as information programs for residents should be considered to complement existing building energy policies. Evidence has shown that behavior-based policies (such as energy use feedback programs) could reduce household energy consumption with little financial investment by either governments or households [54]. Besides, single antecedent-based intervention proved to be not effective as expected in some cases [187], and demographics and motivations of people in different contexts also have to be included in the policy-making of intervention programs for energy efficient behavior. (4) The role of occupant behavior in the effectiveness of building energy efficiency policy remains unclear. Building energy efficiency policy and its outcome is complex and multifaceted. Its effectiveness can be influenced by various factors such as response of the target group to policy, the conditions of stakeholders, the design and implementation process of policy, etcetera. The role of occupant behavior in the effectiveness of building energy efficiency policy is a big research gap. By identifying occupant behavior‘s influence on the effectiveness of relevant policies, policymakers could adjust and improve 29
ACCEPTED MANUSCRIPT building energy efficiency policy for better outcomes.
9. Conclusions
Acknowledgements
ED
M
AN US
CR IP T
Occupant behavior is critical to the energy performance of buildings, which can be an important supplement to the technology approach to improving building energy performance. Recent years have seen an exponential growth in literature focusing on occupant behavior. This paper identified the hot topics, overall research trends, and remaining research gaps by means of bibliometric analysis and in-depth review of literature to date. Four hot-topic research areas have been identified, namely 1) understanding of occupant behavior, especially specific behaviors affecting indoor thermal comfort, such as window opening behavior, lighting control behavior and space heating/cooling behavior; 2) research methods and energy data collection; 3) quantitative modeling of occupant behavior and building energy performance; and 4) energy saving potential and behavioral strategies. Keywords analysis showed a shift of research focus from qualitative research to quantitative analysis. There are four main types of quantitative methods used in modeling occupant behavior: probabilistic modeling, statistical methods, agent-based modeling, and data-mining approaches. The energy-saving potential of occupant behavior in building energy performance ranges between 10%-25% for residential buildings, and 5%-30% for commercial buildings. We concluded by identifying four significant research gaps about occupant behavior and building energy performance: the need for understanding occupant behavior in a systematic framework; for stronger empirical evidence beyond individual buildings and at a larger city scale; for linking occupant behavior to socio-economic and policy variables; and for evaluating the role of occupant behavior in the effectiveness of building energy efficiency policy.
References
PT
This work was partly funded by the China Scholarship Council (CSC) under the Grant No. 201506030015.
AC
CE
[1] A.M. Kazim, Assessments of primary energy consumption and its environmental consequences in the United Arab Emirates, Renewable and Sustainable Energy Reviews, 11 (3) (2007) 426-446. [2] F. Lima, M.L. Nunes, J. Cunha, A.F. Lucena, Driving forces for aggregate energy consumption: A cross-country approach, Renewable and Sustainable Energy Reviews, 68 (2017) 1033-1050. [3] D.R. Easterling, G.A. Meehl, C. Parmesan, S.A. Changnon, T.R. Karl, L.O. Mearns, Climate Extremes: Observations, Modeling, and Impacts, Science, 289 (2000) 2068-2074. [4] M. Reichstein, M. Bahn, P. Ciais, D. Frank, M.D. Mahecha, S.I. Seneviratne, J. Zscheischler, C. Beer, N. Buchmann, D.C. Frank, D. Papale, A. Rammig, P. Smith, K. Thonicke, M. van der Velde, S. Vicca, A. Walz, M. Wattenbach, Climate extremes and the carbon cycle, Nature, 500 (7462) (2013) 287-295. [5] C. Spandagos, T.L. Ng, Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities, Applied Energy, 189 (2017) 352-368. [6] L. Steg, G. Perlaviciute, E. van der Werff, Understanding the human dimensions of a sustainable energy transition, Frontiers in psychology, 6 (2015). [7] M.K. Dixit, J.L. Fernández-Solís, S. Lavy, C.H. Culp, Identification of parameters for embodied energy measurement: A literature review, Energy and Buildings, 42 (8) (2010) 1238-1247. [8] Y. Zhang, C.-Q. He, B.-J. Tang, Y.-M. Wei, China's energy consumption in the building sector: A life cycle approach, Energy and Buildings, 94 (2015) 240-251. [9] E.I.A. (EIA), International Energy Outlook 2016, in, U.S. Department of Energy, May 2016, pp. 30
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
DOE/EIA-0484(2016). [10] O. Lucon, D. Ürge-Vorsatz, A.Z. Ahmed, H. Akbari, P. Bertoldi, L.F. Cabeza, N. Eyre, A. Gadgil, L.D. Harvey, Y. Jiang, E. Liphoto, S. Mirasgedis, S. Murakami, J. Parikh, C. Pyke, M.V. VilariÒo, Chapter 9 - Buildings, in: Climate Change 2014: Mitigation of Climate Change. IPCC Working Group III Contribution to AR5, Cambridge University Press, 2014. [11] L.D. Harvey, Reducing energy use in the buildings sector: measures, costs, and examples, Energy Efficiency, 2 (2) (2009) 139-163. [12] M. Levine, D. Ürge-Vorsatz, K. Blok, L. Geng, D. Harvey, S. Lang, G. Levermore, A. Mongameli Mehlwana, S. Mirasgedis, A. Novikova, J. Rilling, Y. H., Residential and commercial buildings, in: Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2007. [13] M. Asif, Growth and sustainability trends in the buildings sector in the GCC region with particular reference to the KSA and UAE, Renewable and Sustainable Energy Reviews, 55 (2016) 1267-1273. [14] L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy and buildings, 40 (3) (2008) 394-398. [15] I.E.A. (IEA), Energy in Buildings and Communities Programme Annual Report 2016, in, IEA-EBC, 2017. [16] International Energy Agency, Technology Roadmap: Energy Efficient Building Envelopes, in, International Energy Agency, 2013. [17] W. Goetzler, M. Guernsey, J. Young, Research & Development Roadmap for Emerging HVAC Technologies, in, 2014. [18] J.C. Lam, Energy analysis of commercial buildings in subtropical climates, Building and Environment, 35 (1) (2000) 19-26. [19] N. Aste, M. Manfren, G. Marenzi, Building Automation and Control Systems and performance optimization: A framework for analysis, Renewable and Sustainable Energy Reviews, (2016). [20] P.H. Shaikh, N.B.M. Nor, P. Nallagownden, I. Elamvazuthi, T. Ibrahim, A review on optimized control systems for building energy and comfort management of smart sustainable buildings, Renewable and Sustainable Energy Reviews, 34 (2014) 409-429. [21] M. Pothitou, A.J. Kolios, L. Varga, S. Gu, A framework for targeting household energy savings through habitual behavioural change, International Journal of Sustainable Energy, 35 (7) (2016) 686-700. [22] H. Allcott, S. Mullainathan, Behavior and energy policy, Science, 327 (5970) (2010) 1204-1205. [23] H. Allcott, M. Greenstone, Is there an energy efficiency gap?, The Journal of Economic Perspectives, 26 (1) (2012) 3-28. [24] N. Kahma, K. Matschoss, The rejection of innovations? Rethinking technology diffusion and the non-use of smart energy services in Finland, Energy Res. Soc. Sci., 34 (2017) 27-36. [25] G. Branco, B. Lachal, P. Gallinelli, W. Weber, Predicted versus observed heat consumption of a low energy multifamily complex in Switzerland based on long-term experimental data, Energy and Buildings, 36 (6) (2004) 543-555. [26] R.H. Socolow, The Twin Rivers program on energy conservation in housing: Highlights and conclusions, Energy and Buildings, 1 (3) (1978) 207-242. [27] G. Levermore, Monitoring and targeting; motivation and training, in: Energy Management Experience Conference, CICC, Cambridge, UK, 1985, pp. 21-30. [28] M. Schweiker, A. Wagner, The effect of occupancy on perceived control, neutral temperature, and behavioral patterns, Energy and Buildings, 117 (2016) 246-259. [29] S. D‘Oca, T. Hong, J. Langevin, The human dimensions of energy use in buildings: A review, Renewable and Sustainable Energy Reviews, 81 (2018) 731-742. [30] M.C. Gonzalez, C.A. Hidalgo, A.-L. Barabasi, Understanding individual human mobility patterns, nature, 453 (7196) (2008) 779. [31] P.C. Stern, B.K. Sovacool, T. Dietz, Towards a science of climate and energy choices, Nature Climate Change, (2016). [32] J. Schot, L. Kanger, G. Verbong, The roles of users in shaping transitions to new energy systems, Nature Energy, 1 (2016) 16054. [33] T. Dietz, G.T. Gardner, J. Gilligan, P.C. Stern, M.P. Vandenbergh, Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions, Proceedings of the National Academy of Sciences, 106 (44) (2009) 18452-18456. [34] M. Lopes, C. Antunes, N. Martins, Energy behaviours as promoters of energy efficiency: A 21st century review, Renewable and Sustainable Energy Reviews, 16 (6) (2012) 4095-4104. [35] T. Dietz, P.C. Stern, E.U. Weber, Reducing carbon-based energy consumption through changes in household behavior, Daedalus, 142 (1) (2013) 78-89. 31
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
[36] T. Hong, S. D'Oca, W.J. Turner, S.C. Taylor-Lange, An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework, Building and Environment, 92 (2015) 764-777. [37] X. Bai, R.J. Dawson, D. Ürge-Vorsatz, G.C. Delgado, A.S. Barau, S. Dhakal, D. Dodman, L. Leonardsen, V. Masson-Delmotte, D. Roberts, Six research priorities for cities and climate change, Nature, 555 (7694) (2018) 23-25. [38] D. Ürge-Vorsatz, C. Rosenzweig, R.J. Dawson, R.S. Rodriguez, X. Bai, A.S. Barau, K.C. Seto, S. Dhakal, Locking in positive climate responses in cities, Nature Climate Change, 8 (3) (2018) 174. [39] D. Ürge-Vorsatz, N. Eyre, P. Graham, D. Harvey, E. Hertwich, Y. Jiang, C. Kornevall, M. Majumdar, J. McMahon, S. Mirasgedis, Chapter 10-energy end-use: building, Global Energy Assessment-Toward a Sustainable Future, (2012) 649-760. [40] A. Grubler, X. Bai, T. Buettner, S. Dhakal, D.J. Fisk, T. Ichinose, J.E. Keirstead, G. Sammer, D. Satterthwaite, N.B. Schulz, N. Shah, J. Steinberger, H. Weisz, Chapter 18 - Urban Energy Systems, in: Global Energy Assessment - Toward a Sustainable Future, Cambridge University Press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria, 2012, pp. 1307-1400. [41] T. Hong, D. Yan, S. D'Oca, C.-f. Chen, Ten questions concerning occupant behavior in buildings: The big picture, Building and Environment, 114 (2017) 518-530. [42] R.K. Jain, R. Gulbinas, J.E. Taylor, P.J. Culligan, Can social influence drive energy savings? Detecting the impact of social influence on the energy consumption behavior of networked users exposed to normative eco-feedback, Energy and Buildings, 66 (2013) 119-127. [43] L. Steg, Promoting household energy conservation, Energy Policy, 36 (12) (2008) 4449-4453. [44] F. Stazi, F. Naspi, M. D'Orazio, A literature review on driving factors and contextual events influencing occupants' behaviours in buildings, Building and Environment, 118 (2017) 40-66. [45] A. Roetzel, A. Tsangrassoulis, U. Dietrich, S. Busching, A review of occupant control on natural ventilation, Renew. Sust. Energ. Rev., 14 (3) (2010) 1001-1013. [46] V. Fabi, R.V. Andersen, S. Corgnati, B.W. Olesen, Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models, Building and Environment, 58 (2012) 188-198. [47] A.L. Pisello, V.L. Castaldo, C. Piselli, C. Fabiani, F. Cotana, How peers' personal attitudes affect indoor microclimate and energy need in an institutional building: Results from a continuous monitoring campaign in summer and winter conditions, Energy and Buildings, 126 (2016) 485-497. [48] J.L. Ouyang, K. Hokao, Energy-saving potential by improving occupants' behavior in urban residential sector in Hangzhou City, China, Energy and Buildings, 41 (7) (2009) 711-720. [49] G. Peschiera, J.E. Taylor, J.A. Siegel, Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data, Energy and Buildings, 42 (8) (2010) 1329-1336. [50] G. Peschiera, J.E. Taylor, The impact of peer network position on electricity consumption in building occupant networks utilizing energy feedback systems, Energy and Buildings, 49 (2012) 584-590. [51] J. Chen, J.E. Taylor, H.-H. Wei, Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation, Energy and Buildings, 47 (2012) 515-524. [52] J. Chen, R.K. Jain, J.E. Taylor, Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption, Applied energy, 105 (2013) 358-368. [53] R.M. Tetlow, C.P. Beaman, A.A. Elmualim, K. Couling, Simple prompts reduce inadvertent energy consumption from lighting in office buildings, Building and Environment, 81 (2014) 234-242. [54] M.A. Delmas, M. Fischlein, O.I. Asensio, Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012, Energy Policy, 61 (2013) 729-739. [55] Q.D. Li, X. Sun, C. Chen, X.D. Yang, Characterizing the household energy consumption in heritage Nanjing Tulou buildings, China: A comparative field survey study, Energy and Buildings, 49 (2012) 317-326. [56] P. Jiang, Y.H. Chen, B. Xu, W.B. Dong, E. Kennedy, Building low carbon communities in China: The role of individual's behaviour change and engagement, Energy Policy, 60 (2013) 611-620. [57] S. Hu, D. Yan, S. Guo, Y. Cui, B. Dong, A survey on energy consumption and energy usage behavior of households and residential building in urban China, Energy and Buildings, (2017). [58] S. Marzban, L. Ding, V. Timchenko, M. Irger, Façade Optimization in a Wind-Driven Ventilated Residential Building Targeting Thermal Comfort, IAQ and Energy Consumption, International Journal of Environmental Science and Development, 7 (5) (2016) 379. 32
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
[59] W. Zhao, L. Ding, P. Cooper, P. Perez, Smart home electricity management in the context of local power resources and smart grid, (2014). [60] X. Zhou, D. Yan, X. Feng, G. Deng, Y. Jian, Y. Jiang, Influence of household air-conditioning use modes on the energy performance of residential district cooling systems, Build. Simul., 9 (4) (2016) 429-441. [61] G.Y. Yun, J. Choi, J.T. Kim, Energy performance of direct expansion air handling unit in office buildings, Energy and Buildings, 77 (2014) 425-431. [62] G.Y. Yun, K. Steemers, N. Baker, Natural ventilation in practice: linking facade design, thermal performance, occupant perception and control, Build. Res. Informat., 36 (6) (2008) 608-624. [63] A. Stafford, D. Lilley, Predicting in situ heat pump performance: An investigation into a single ground-source heat pump system in the context of 10 similar systems, Energy and Buildings, 49 (2012) 536-541. [64] M. Scarpa, U. Mazzali, F. Peron, Modeling the energy performance of living walls: Validation against field measurements in temperate climate, Energy and Buildings, 79 (2014) 155-163. [65] D. Saelens, W. Parys, R. Baetens, Energy and comfort performance of thermally activated building systems including occupant behavior, Building and Environment, 46 (4) (2011) 835-848. [66] A. Roetzel, A. Tsangrassoulis, U. Dietrich, Impact of building design and occupancy on office comfort and energy performance in different climates, Building and Environment, 71 (2014) 165-175. [67] O. Mejri, E.P. Del Barrio, N. Ghrab-Morcos, Energy performance assessment of occupied buildings using model identification techniques, Energy and Buildings, 43 (2-3) (2011) 285-299. [68] A. Ioannou, L.C.M. Itard, Energy performance and comfort in residential buildings: Sensitivity for building parameters and occupancy, Energy and Buildings, 92 (2015) 216-233. [69] M. Sunikka-Blank, R. Galvin, Introducing the prebound effect: the gap between performance and actual energy consumption, Build. Res. Informat., 40 (3) (2012) 260-273. [70] D.A. Majcen, L. Itard, H. Visscher, Statistical model of the heating prediction gap in Dutch dwellings: Relative importance of building, household and behavioural characteristics, Energy and Buildings, 105 (2015) 43-59. [71] I. Gaetani, P.J. Hoes, J.L.M. Hensen, Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy, Energy and Buildings, 121 (2016) 188-204. [72] L.K. Norford, R.H. Socolow, E.S. Hsieh, G.V. Spadaro, 2-TO-ONE DISCREPANCY BETWEEN MEASURED AND PREDICTED PERFORMANCE OF A LOW-ENERGY OFFICE BUILDING INSIGHTS FROM A RECONCILIATION BASED ON THE DOE-2 MODEL, Energy and Buildings, 21 (2) (1994) 121-131. [73] E. Azar, C.C. Menassa, Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings, J. Comput. Civil. Eng., 26 (4) (2012) 506-518. [74] M.D. Jia, R.S. Srinivasan, A.A. Raheem, From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency, Renew. Sust. Energ. Rev., 68 (2017) 525-540. [75] A.C. Menezes, A. Cripps, D. Bouchlaghem, R. Buswell, Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap, Applied Energy, 97 (2012) 355-364. [76] K. Schakib-Ekbatan, F.Z. Cakici, M. Schweiker, A. Wagner, Does the occupant behavior match the energy concept of the building? - Analysis of a German naturally ventilated office building, Building and Environment, 84 (2015) 142-150. [77] U.I. Dar, L. Georges, I. Sartori, V. Novakovic, Influence of occupant's behavior on heating needs and energy system performance: A case of well-insulated detached houses in cold climates, Build. Simul., 8 (5) (2015) 499-513. [78] C.L. Borgman, J. Furner, Scholarly communication and bibliometrics, Annual review of information science and technology, 36 (1) (2002) 2-72. [79] T.U. Daim, G. Rueda, H. Martin, P. Gerdsri, Forecasting emerging technologies: Use of bibliometrics and patent analysis, Technological Forecasting and Social Change, 73 (8) (2006) 981-1012. [80] Y.-M. Wei, Z.-F. Mi, Z. Huang, Climate policy modeling: an online SCI-E and SSCI based literature review, Omega, 57 (2015) 70-84. [81] H. Yu, Y.-M. Wei, B.-J. Tang, Z. Mi, S.-Y. Pan, Assessment on the research trend of low-carbon energy technology investment: A bibliometric analysis, Applied energy, 184 (2016) 960-970. [82] R.H. Socolow, TWIN RIVERS PROGRAM ON ENERGY-CONSERVATION IN HOUSING HIGHLIGHTS AND CONCLUSIONS, Energy and Buildings, 1 (3) (1978) 207-242. [83] C. Seligman, J.M. Darley, L.J. Becker, Behavioral approaches to residential energy conservation, 33
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Energy and buildings, 1 (3) (1978) 325-337. [84] R.C. Sonderegger, Movers and stayers: The resident's contribution to variation across houses in energy consumption for space heating, Energy and Buildings, 1 (3) (1978) 313-324. [85] D. Yan, T. Hong, IEA EBC Annex 66: Definition and Simulation of Occupant Behavior in Buildings, in, 2013. [86] P. Hoes, J.L.M. Hensen, M. Loomans, B. de Vries, D. Bourgeois, User behavior in whole building simulation, Energy and Buildings, 41 (3) (2009) 295-302. [87] Z. Yu, B.C.M. Fung, F. Haghighat, H. Yoshino, E. Morofsky, A systematic procedure to study the influence of occupant behavior on building energy consumption, Energy and Buildings, 43 (6) (2011) 1409-1417. [88] I. Richardson, M. Thomson, D. Infield, A high-resolution domestic building occupancy model for energy demand simulations, Energy and Buildings, 40 (8) (2008) 1560-1566. [89] J. Widen, E. Wackelgard, A high-resolution stochastic model of domestic activity patterns and electricity demand, Applied Energy, 87 (6) (2010) 1880-1892. [90] D. Bourgeois, C. Reinhart, I. Macdonald, Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control, Energy and Buildings, 38 (7) (2006) 814-823. [91] R.V. Jones, A. Fuertes, E. Gregori, A. Giretti, Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings, Building and Environment, 118 (2017) 144-158. [92] D. Yan, W. O'Brien, T.Z. Hong, X.H. Feng, H.B. Gunay, F. Tahmasebi, A. Mahdavi, Occupant behavior modeling for building performance simulation: Current state and future challenges, Energy and Buildings, 107 (2015) 264-278. [93] S.Q. Chen, W.W. Yang, H. Yoshino, M.D. Levine, K. Newhouse, A. Hinge, Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies, Energy and Buildings, 104 (2015) 1-13. [94] M. Costanzo, D. Archer, E. Aronson, T. Pettigrew, Energy conservation behavior: The difficult path from information to action, American psychologist, 41 (5) (1986) 521. [95] R.V. Andersen, J. Toftum, K.K. Andersen, B.W. Olesen, Survey of occupant behaviour and control of indoor environment in Danish dwellings, Energy and Buildings, 41 (1) (2009) 11-16. [96] Z.M. Gill, M.J. Tierney, I.M. Pegg, N. Allan, Low-energy dwellings: the contribution of behaviours to actual performance, Build. Res. Informat., 38 (5) (2010) 491-508. [97] H.B. Gunay, W. O'Brien, I. Beausoleil-Morrison, A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices, Building and Environment, 70 (2013) 31-47. [98] S. Gauthier, Investigating the probability of behavioural responses to cold thermal discomfort, Energy and Buildings, 124 (2016) 70-78. [99] H. Polinder, M. Schweiker, A. van der Aa, K. Schakib-Ekbatan, V. Fabi, R. Andersen, Final Report Annex 53, Volume II Occupant Behavior and Modeling, Retrieved from Tohoku http:// www. iea-ebc. org/ index. php, (2013). [100] G.T. Gardner, P.C. Stern, Environmental problems and human behavior, Allyn & Bacon, 1996. [101] G. Wood, M. Newborough, Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design, Energy and buildings, 35 (8) (2003) 821-841. [102] J. Langevin, P.L. Gurian, J. Wen, Reducing energy consumption in low income public housing: Interviewing residents about energy behaviors, Applied Energy, 102 (2013) 1358-1370. [103] D. Cali, R.K. Andersen, D. Mueller, B.W. Olesen, Analysis of occupants' behavior related to the use of windows in German households, Building and Environment, 103 (2016) 54-69. [104] Y.W. Jian, Y.J. Guo, J. Liu, Z. Bai, Q.R. Li, Case study of window opening behavior using field measurement results, Build. Simul., 4 (2) (2011) 107-116. [105] V. Fabi, R.K. Andersen, S. Corgnati, Verification of stochastic behavioural models of occupants' interactions with windows in residential buildings, Building and Environment, 94 (2015) 371-383. [106] V. Fabi, R.K. Andersen, S. Corgnati, Accounting for the Uncertainty Related to Building Occupants with Regards to Visual Comfort: A Literature Survey on Drivers and Models, Buildings, 6 (1) (2016) 17. [107] F. Haldi, D. Robinson, The impact of occupants' behaviour on building energy demand, J. Build. Perf. Simul., 4 (4) (2011) 323-338. [108] C.C. Menassa, N. Taylor, J. Nelson, Optimizing hybrid ventilation in public spaces of complex buildings–a case study of the Wisconsin Institutes for Discovery, Building and Environment, 61 (2013) 57-68. [109] S.P. Corgnati, S. D'Oca, V. Fabi, R.K. Andersen, Leverage of Behavioural Patterns of Window 34
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
Opening and Heating Set Point Adjustments on Energy Consumption and Thermal Comfort in Residential Buildings, in: A. Li, Y. Zhu, Y. Li (Eds.) Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning, Vol 1: Indoor and Outdoor Environment, Vol. 261, Springer-Verlag Berlin, Berlin, 2014, pp. 23-31. [110] L.P. Wang, S. Greenberg, Window operation and impacts on building energy consumption, Energy and Buildings, 92 (2015) 313-321. [111] A. Heydarian, E. Pantazis, J.P. Carneiro, D. Gerber, B. Becerik-Gerber, Lights, building, action: Impact of default lighting settings on occupant behaviour, J. Environ. Psychol., 48 (2016) 212-223. [112] A. Heydarian, J.P. Carneiro, D. Gerber, B. Becerik-Gerber, Immersive virtual environments, understanding the impact of design features and occupant choice upon lighting for building performance, Building and Environment, 89 (2015) 217-228. [113] C.E. Ochoa, M.B.C. Aries, J.L.M. Hensen, State of the art in lighting simulation for building science: a literature review, J. Build. Perf. Simul., 5 (4) (2012) 209-233. [114] G.Y. Yun, H. Kim, J.T. Kim, Effects of occupancy and lighting use patterns on lighting energy consumption, Energy and Buildings, 46 (2012) 152-158. [115] G.Y. Yun, H.J. Kong, H. Kim, J.T. Kim, A field survey of visual comfort and lighting energy consumption in open plan offices, Energy and Buildings, 46 (2012) 146-151. [116] X. Zhou, D. Yan, T.Z. Hong, X.X. Ren, Data analysis and stochastic modeling of lighting energy use in large office buildings in China, Energy and Buildings, 86 (2015) 275-287. [117] H.B. Gunay, W. O'Brien, I. Beausoleil-Morrison, S. Gilani, Development and implementation of an adaptive lighting and blinds control algorithm, Building and Environment, 113 (2017) 185-199. [118] C. Wang, D. Yan, X. Ren, Modeling Individual's Light Switching Behavior to Understand Lighting Energy Use of Office Building, Energy Procedia, 88 (2016) 781-787. [119] A.D. Galasiu, M.R. Atif, Applicability of daylighting computer modeling in real case studies: comparison between measured and simulated daylight availability and lighting consumption, Building and Environment, 37 (4) (2002) 363-377. [120] D.H. Li, J.C. Lam, Evaluation of lighting performance in office buildings with daylighting controls, Energy and Buildings, 33 (8) (2001) 793-803. [121] D. Lindelöf, N. Morel, A field investigation of the intermediate light switching by users, Energy and Buildings, 38 (7) (2006) 790-801. [122] D. Ürge-Vorsatz, L.F. Cabeza, S. Serrano, C. Barreneche, K. Petrichenko, Heating and cooling energy trends and drivers in buildings, Renewable and Sustainable Energy Reviews, 41 (2015) 85-98. [123] Z. Wang, Z. Zhao, B.R. Lin, Y.X. Zhu, Q. Ouyang, Residential heating energy consumption modeling through a bottom-up approach for China's Hot Summer-Cold Winter climatic region, Energy and Buildings, 109 (2015) 65-74. [124] B. Dong, M.B. Kjærgaard, M.D. Simone, H.B. Gunay, W. O‘Brien, D. Mora, J. Dziedzic, J. Zhao, Sensing and Data Acquisition, in: W. Andreas, W. O‘Brien, B. Dong. (Eds.) Exploring Occupant Behavior in Buildings: Methods and Challenges, Springer International Publishing AG, 2017. [125] M. Jia, R.S. Srinivasan, Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies, in: Proceedings of the 2015 Winter Simulation Conference, IEEE Press, 2015, pp. 3345-3355. [126] V.L. Castaldo, A.L. Pisello, Uses of dynamic simulation to predict thermal‐ energy performance of buildings and districts: a review, Wiley Interdisciplinary Reviews: Energy and Environment, 7 (1) (2018). [127] P.X. Zou, X. Xu, J. Sanjayan, J. Wang, A mixed methods design for building occupants‘ energy behavior research, Energy and Buildings, 166 (2018) 239-249. [128] V.L. Erickson, M.Á. Carreira-Perpiñán, A.E. Cerpa, OBSERVE: Occupancy-based system for efficient reduction of HVAC energy, in: Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on, IEEE, 2011, pp. 258-269. [129] B. Dong, K.P. Lam, Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network, J. Build. Perf. Simul., 4 (4) (2011) 359-369. [130] C. Lee, J. Tong, V. Cheng, Occupant behavior in building design and operation, in: HK joint Symposium 2014, 2014. [131] Z. Chen, J. Xu, Y.C. Soh, Modeling regular occupancy in commercial buildings using stochastic models, Energy and Buildings, 103 (2015) 216-223. [132] J. Virote, R. Neves-Silva, Stochastic models for building energy prediction based on occupant behavior assessment, Energy and Buildings, 53 (2012) 183-193. [133] T. Zaraket, B. Yannou, Y. Leroy, S. Minel, E. Chapotot, A Stochastic activity-based approach for forecasting occupant-related energy consumption in residential buildings, in: ASME 2014 35
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2014, pp. V004T006A034-V004T006A034. [134] F.R. Cecconi, M. Manfren, L.C. Tagliabue, A.L.C. Ciribini, E. De Angelis, Probabilistic behavioural modeling in building performance simulation: A Monte Carlo approach, Energy and Buildings, (2017). [135] G. Flett, N. Kelly, A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand, Energy and Buildings, 140 (2017) 171-187. [136] C.M. Stoppel, F. Leite, Integrating probabilistic methods for describing occupant presence with building energy simulation models, Energy and Buildings, 68 (2014) 99-107. [137] F. Haldi, D. Robinson, On the behaviour and adaptation of office occupants, Building and environment, 43 (12) (2008) 2163-2177. [138] A. Mahdavi, C. Pröglhöf, User behavior and energy performance in buildings, Wien, Austria: Internationalen Energiewirtschaftstagung an Der TU Wien (IEWT), (2009) 1-13. [139] R. Andersen, V. Fabi, J. Toftum, S.P. Corgnati, B.W. Olesen, Window opening behaviour modelled from measurements in Danish dwellings, Building and Environment, 69 (2013) 101-113. [140] C. Peng, D. Yan, R.H. Wu, C. Wang, X. Zhou, Y. Jiang, Quantitative description and simulation of human behavior in residential buildings, Build. Simul., 5 (2) (2012) 85-94. [141] V. Tabak, B. de Vries, Methods for the prediction of intermediate activities by office occupants, Building and Environment, 45 (6) (2010) 1366-1372. [142] N. Li, J.C. Li, R.J. Fan, H.Y. Jia, Probability of occupant operation of windows during transition seasons in office buildings, Renew. Energy, 73 (2015) 84-91. [143] A. Mahdavi, F. Tahmasebi, Predicting people's presence in buildings: An empirically based model performance analysis, Energy and Buildings, 86 (2015) 349-355. [144] L. Klein, J.-y. Kwak, G. Kavulya, F. Jazizadeh, B. Becerik-Gerber, P. Varakantham, M. Tambe, Coordinating occupant behavior for building energy and comfort management using multi-agent systems, Automation in Construction, 22 (2012) 525-536. [145] A. Alfakara, B. Croxford, Using agent-based modelling to simulate occupants' behaviours in response to summer overheating, in: Proceedings of the Symposium on Simulation for Architecture & Urban Design, Society for Computer Simulation International, 2014, pp. 13. [146] Y.S. Lee, A.M. Malkawi, Simulating multiple occupant behaviors in buildings: An agent-based modeling approach, Energy and Buildings, 69 (2014) 407-416. [147] J. Langevin, J. Wen, P.L. Gurian, Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors, Building and Environment, 88 (2015) 27-45. [148] E. Azar, C.C. Menassa, Agent-based modeling of occupants and their impact on energy use in commercial buildings, J. Comput. Civil. Eng., 26 (4) (2011) 506-518. [149] C. Liao, P. Barooah, An integrated approach to occupancy modeling and estimation in commercial buildings, in: American Control Conference (ACC), 2010, IEEE, 2010, pp. 3130-3135. [150] J. Zhao, B. Lasternas, K.P. Lam, R. Yun, V. Loftness, Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining, Energy and Buildings, 82 (2014) 341-355. [151] S. D'Oca, T.Z. Hong, Occupancy schedules learning process through a data mining framework, Energy and Buildings, 88 (2015) 395-408. [152] A. Alhamoud, P. Xu, F. Englert, A. Reinhardt, P. Scholl, D. Boehnstedt, R. Steinmetz, Extracting human behavior patterns from appliance-level power consumption data, in: European Conference on Wireless Sensor Networks, Springer, 2015, pp. 52-67. [153] M. Baptista, A. Fang, H. Prendinger, R. Prada, Y. Yamaguchi, Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques, in: AAAI, 2014, pp. 1164-1170. [154] X.X. Ren, D. Yan, T.Z. Hong, Data mining of space heating system performance in affordable housing, Building and Environment, 89 (2015) 1-13. [155] Z. Yu, F. Haghighat, B.C.M. Fung, E. Morofsky, H. Yoshino, A methodology for identifying and improving occupant behavior in residential buildings, Energy, 36 (11) (2011) 6596-6608. [156] D. Aerts, J. Minnen, I. Glorieux, I. Wouters, F. Descamps, A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison, Building and Environment, 75 (2014) 67-78. [157] P.D. Andersen, A. Iversen, H. Madsen, C. Rode, Dynamic modeling of presence of occupants using inhomogeneous Markov chains, Energy and Buildings, 69 (2014) 213-223. [158] C.F. Reinhart, Lightswitch-2002: a model for manual and automated control of electric lighting and blinds, Sol. Energy, 77 (1) (2004) 15-28. 36
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
[159] J. Liisberg, J.K. Møller, H. Bloem, J. Cipriano, G. Mor, H. Madsen, Hidden Markov Models for indirect classification of occupant behaviour, Sust. Cities Soc., 27 (2016) 83-98. [160] O.G. Santin, Behavioural Patterns and User Profiles related to energy consumption for heating, Energy and Buildings, 43 (10) (2011) 2662-2672. [161] F. McLoughlin, A. Duffy, M. Conlon, Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study, Energy and Buildings, 48 (2012) 240-248. [162] T. Labeodan, K. Aduda, G. Boxem, W. Zeiler, On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction - A survey, Renew. Sust. Energ. Rev., 50 (2015) 1405-1414. [163] H. Zhou, L.F. Qiao, Y. Jiang, H.J. Sun, Q.Y. Chen, Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach, Energy and Buildings, 111 (2016) 233-241. [164] I.P.o.C. Change, Climate Change 2007-Mitigation of Climate Change: Working Group III Contribution to the Fourth Assessment Report of the IPCC, Cambridge University Press, 2007. [165] B.E. Metz, Climate change 2001: mitigation: contribution of Working Group III to the third assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 2001. [166] W. Kleiminger, F. Mattern, S. Santini, Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches, Energy and Buildings, 85 (2014) 493-505. [167] T. Hong, S.C. Taylor-Lange, S. D'Oca, D. Yan, S.P. Corgnati, Advances in research and applications of energy-related occupant behavior in buildings, Energy and Buildings, 116 (2016) 694-702. [168] E. Azar, C.C. Menassa, A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks, Energy Policy, 67 (2014) 459-472. [169] A.R. Carrico, M. Riemer, Motivating energy conservation in the workplace: An evaluation of the use of group-level feedback and peer education, J. Environ. Psychol., 31 (1) (2011) 1-13. [170] R.A. Winett, J.H. Kagel, Effects of information presentation format on resource use in field studies, J. Consum. Res., 11 (2) (1984) 655-667. [171] M.L. Dennis, E.J. Soderstrom, W.S. Koncinski, B. Cavanaugh, Effective dissemination of energy-related information: Applying social psychology and evaluation research, American Psychologist, 45 (10) (1990) 1109. [172] O.G. Santin, Occupant behaviour in energy efficient dwellings: evidence of a rebound effect, J. Hous. Built Environ., 28 (2) (2013) 311-327. [173] J.D. Khazzoom, Economic implications of mandated efficiency in standards for household appliances, The energy journal, 1 (4) (1980) 21-40. [174] E.G. Hertwich, Consumption and the rebound effect: An industrial ecology perspective, Journal of industrial ecology, 9 (1‐ 2) (2005) 85-98. [175] P.H. Berkhout, J.C. Muskens, J.W. Velthuijsen, Defining the rebound effect, Energy Policy, 28 (6) (2000) 425-432. [176] V. Tiefenbeck, T. Staake, K. Roth, O. Sachs, For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign, Energy Policy, 57 (2013) 160-171. [177] S.Z. Attari, M.L. DeKay, C.I. Davidson, W.B. De Bruin, Public perceptions of energy consumption and savings, Proceedings of the National Academy of sciences, 107 (37) (2010) 16054-16059. [178] C.J. Midden, F.G. Kaiser, L. Teddy McCalley, Technology's four roles in understanding individuals' conservation of natural resources, Journal of Social Issues, 63 (1) (2007) 155-174. [179] K. Gillingham, M.J. Kotchen, D.S. Rapson, G. Wagner, Energy policy: The rebound effect is overplayed, Nature, 493 (7433) (2013) 475-476. [180] L.A. Greening, D.L. Greene, C. Difiglio, Energy efficiency and consumption—the rebound effect—a survey, Energy Policy, 28 (6) (2000) 389-401. [181] Z. O'Neill, F.X. Niu, Uncertainty and sensitivity analysis of spatio-temporal occupant behaviors on residential building energy usage utilizing Karhunen-Loeve expansion, Building and Environment, 115 (2017) 157-172. [182] G. Buttitta, W. Turner, D. Finn, Clustering of household occupancy profiles for archetype building models, in: R. Howlett, A. Capozzoli, V. Serra (Eds.) 8th International Conference on Sustainability in Energy and Buildings, Seb-16, Vol. 111, Elsevier Science Bv, Amsterdam, 2017, pp. 171-180. [183] Y.S. Chiou, K.M. Carley, C.I. Davidson, M.P. Johnson, A high spatial resolution residential 37
ACCEPTED MANUSCRIPT
AC
CE
PT
ED
M
AN US
CR IP T
energy model based on American Time Use Survey data and the bootstrap sampling method, Energy and Buildings, 43 (12) (2011) 3528-3538. [184] E.I.A. (EIA), Consumption surveys: Residential Energy Consumption Survey (RECS) and Commercial Buildings Energy Consumption Survey (CBECS), in, https://www.eia.gov/consumption/, 2015. [185] X. Bai, S. Dhakal, J.K. Steinberger, H. Weisz, Drivers of urban energy use and main policy leverages, in: Energizing Sustainable Cities, Routledge, Taylor & Francis Group, 2013. [186] T. Lin, Y. Yu, X. Bai, L. Feng, J. Wang, Greenhouse gas emissions accounting of urban residential consumption: a household survey based approach, PloS one, 8 (2) (2013) e55642. [187] W. Abrahamse, L. Steg, C. Vlek, T. Rothengatter, A review of intervention studies aimed at household energy conservation, J. Environ. Psychol., 25 (3) (2005) 273-291.
38