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Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling
Applied Energy 183 (2016) 926–937
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling Elie Azar ⇑, Christina Nikolopoulou, Sokratis Papadopoulos Engineering Systems and Management Department, Masdar Institute, Abu Dhabi, United Arab Emirates
h i g h l i g h t s An ABM framework is proposed to optimize key building performance metrics. People movement, energy consumption and thermal comfort are efficiently integrated. The framework is based on validated concepts from literature and actual data. An application on a campus environment identifies an optimal HVAC strategy. Energy savings of 19% are observed without compromising thermal comfort levels.
a r t i c l e
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Article history: Received 26 April 2016 Received in revised form 30 August 2016 Accepted 8 September 2016
Keywords: Sustainable building performance Energy consumption Indoor and outdoor thermal comfort People movements Group of buildings Agent-based modeling (ABM)
a b s t r a c t Sustainable building performance requires the integration of various metrics such as energy consumption, thermal comfort levels, occupants’ wellbeing, and productivity. Despite their interdependence, these metrics have been mostly evaluated independently, overlooking potential tradeoffs that can occur between them (e.g., energy conservation efforts and thermal comfort). In addition, human-related factors such as occupants’ energy consumption behaviors, schedules, and movements between buildings cannot be captured using current commercial building modeling tools. Consequently, simulating the performance of a group of buildings such as in a campus, neighborhood, or city remains very challenging. In this paper, a comprehensive agent-based modeling (ABM) framework is developed to: (1) model an urban area with several buildings along with the movements and actions of people within the environment; (2) calculate key performance metrics such as indoor/outdoor thermal comfort and energy consumption levels; and (3) test and propose strategies to optimize sustainable building operation. This study illustrates the multidisciplinary approach needed to capture various dimensions of sustainable building performance. The framework is then applied to a green campus environment, identifying an energy management strategy that can reduce energy consumption by 19% without compromising occupants’ comfort and wellbeing. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction 1.1. Background Buildings account for more than a third of the world’s total end use of energy and subsequent greenhouse gas emissions [1]. This sector typically presents the largest potential for large-scale energy savings, when compared to other sectors of the economy such as the industrial or transportation sectors [2]. This has motivated extensive research on the means to reduce the energy intensity of the built environment, which has traditionally been achieved ⇑ Corresponding author. E-mail address: [email protected] (E. Azar). http://dx.doi.org/10.1016/j.apenergy.2016.09.022 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.
through the research and promotion of energy efficient building systems and technologies, control strategies, and retrofits [3–10]. In recent years, energy conservation efforts have also expanded to include human-focused solutions or interventions such energy education, feedback, and green social marketing campaigns, which promote and incentivize energy conservation actions among building occupants [11–16]. In addition to energy conservation studies, occupants’ comfort and well-being is another building performance metric that has been studied extensively. It is estimated that on average people spend between 80 and 90 percent of their life indoors, which makes the environmental conditions of the built environment a key driver to occupants’ well-being, health, and productivity [17]. A large body of literature can therefore be found on topics such
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as indoor air quality (IAQ) and thermal comfort, as well as related building standards and regulations such as the American Society of Heating, Refrigerating and Air-Conditions Engineers’ (ASHRAE) standards 55-2013 and 62.1-2013 [4,18–21]. Previous studies in this area have largely focused on individual buildings, however, there is a recent trend in the literature to investigate the performance of a group or stock of buildings, tackling the overall sustainability challenges facing the urban environment (e.g., campus, neighborhood, or city levels) [22–26]. The increase in scope has introduced new elements or dimensions of building performance that also need to be considered. These include the movement patterns and schedules of people, outdoor environmental conditions and walkability, and urban design [22–24]. Optimizing building operations requires a comprehensive understanding and integration of the multiple performance metrics covered above [27]. Such integration is specifically important given the interdependencies and potential tradeoffs between various metrics. For instance, it has been shown that energy consumption and indoor thermal conditions are directly related and often conflict with one another [28]. Any energy conservation efforts, such as adjusting thermostat set point temperatures, should therefore ensure that occupants’ thermal comfort and productivity are not compromised. As another example, the schedule and movement of people within and between buildings (e.g., students on campus) has a direct impact on their overall or ‘‘urban” performance [29–31]. Integrating occupancy patterns in building energy performance predictions is therefore essential to develop accurate energy predictions of the buildings under investigation. 1.2. Problem statement A large body of research exists on individual metrics of building performance, however, studies that integrate these elements are still very scarce in literature. Consequently, researchers are facing important limitations to study and optimize the performance of the built environment in a holistic manner, to address its complex and multifaceted sustainability challenges [32–34]. A key contributor to the observed lack of integration is the absence of building modeling frameworks or tools capable of capturing how people use their built environment, how they affect it, and how they are affected by it. Currently, designers and engineers rely mainly on building performance simulation software (e.g., EnergyPlus, Integrated Environmental Solutions (IES), TranSys, eQuest) to model and predict the energy consumption levels of buildings [35]. Despite their robust representation of building systems and components, such simulation software present significant limitations in the modeling of occupant-related attributes and corresponding performance metrics [23,36–39]. More specifically, these tools assume common and constant attributes for all occupants, failing to distinguish between the characteristics of individual occupants (e.g., the differing schedules and energy-use operation patterns of various building systems). Furthermore, they fail to capture occupant-related performance metrics such as personal indoor and outdoor thermal comfort levels, which highly depend on the movements and interactions of people with their built environment [36–40]. 1.3. Objectives A novel agent-based modeling (ABM) framework is proposed to evaluate the performance of the built environment using a holistic and integrated human-centered approach. The framework is unique in its capability to integrate key dimensions of building performance such as the movement and scheduling of people, weather conditions, urban form (e.g., the distance between
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buildings), indoor and outdoor thermal comfort levels, and building energy consumption. The lower-level objectives of this research and the proposed framework are as follows: (1) model an urban area with several buildings along with the movements and actions of people within the environment; (2) calculate key performance metrics such as indoor/outdoor thermal comfort and energy consumption levels; and (3) test and propose building operation strategies to optimize sustainable building performance. A case study from a green university campus in Abu Dhabi, UAE, is then presented. The framework is specifically applied to simulate occupants’ movements and schedules, their indoor and outdoor thermal comfort levels, as well as the buildings’ energy consumption levels. The results helped to successfully identify an optimal heating, ventilation and air-conditioning (HVAC) strategy that maximizes both energy savings and thermal comfort levels. The contributions of this work are significant and include its ability to capture individual occupants’ characteristics and the resulting two-way interaction they can have with their environment. This helps address the lack of integration of the ‘‘human” or ‘‘individual” dimension in traditional building modeling tools and studies, setting the stage for an integrated human-in-theloop building modeling approach. Consequently, specific strategies can be identified to optimize metrics of sustainable building performance and to minimize the tradeoffs that may exist between them (e.g., energy consumption and occupants’ thermal comfort). Such strategies can be directly employed by stakeholders, including facility managers, owners, and policy makers, to improve the performance of their built environment. Finally, a broader contribution of this work is its ability to integrate various disciplines in building performance evaluation. These include – but are not limited to – building simulation and design, indoor and outdoor environmental conditions, walkability evaluation, urban planning, social and psychological aspects of energy consumption, as well as occupants’ comfort, wellbeing, and potentially productivity. Despite the growing interest of the scientific community to integrate methods and results from these fields, current literature lacks a modeling framework that allows for such an integration. The proposed framework, which is presented later, is designed to address this need, combining validated concepts and principles from various disciplines, as well as data collected from a large number of buildings. 2. Literature review Prior to proceeding with the methodology, this section summarizes literature on the main dimensions of building performance considered in this study. The dimensions include energy consumption, human comfort, and human movement in urban environments and communities. 2.1. Energy consumption and occupant behavior In the past number of decades, significant research efforts have focused on improving the efficiency and design of various buildings systems such as HVAC, lighting, office equipment, and home appliances [8–10,41–43]. A special emphasis has also been placed on building energy management and automation systems, which have shown to achieve important energy savings [3,44–47]. In parallel to research on these ‘‘active” energy saving strategies, ‘‘passive” design strategies have also been studied and developed. These mainly include building design characteristics that can passively, or indirectly, reduce energy consumption levels (e.g., building orientation, glazing type, window-to-wall ratio (WWR), window shading, wall insulation, ‘cool’ roof surface materials, and thermal mass [48–53].
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In addition to this, a human-centered energy conservation approach has also emerged in recent years, with the goal of altering current energy consumption patterns of occupants and/or facility managers [13–16,39,54–58]. The need for such an approach derives from studies directly linking building performance to how people operate and control its various systems. Consequently, any building – including a low-energy or green-certified building – runs the risk of over consuming energy if it is not operated efficiently. In general, studies on the human role in energy conservation can be divided into four categories. The first category includes studies that quantify the impact of individual human actions on building performance through observations or simulations [55–61]. For instance, Sanchez et al. [60] and Webber et al. [61] observed the energy consumption patterns of commercial building occupants and found that typically more than 50% of office equipment is left running after-hours (i.e., when the building is unoccupied). In parallel, Azar and Menassa [58] conducted sensitivity analyses on building energy models to quantify the impact of individual occupants’ actions on the energy consumption of office buildings (e.g., thermostat set point settings, lighting and equipment usage patterns). The second category tests occupancy intervention methods aiming to alter current energy consumption behaviors (e.g., energy education, feedback methods, and green social marketing campaigns) [11,13–16]. For instance, feedback methods consist of sharing building energy consumption information with occupants, as this has shown to affect current behaviors [62,63]. Peschiera et al. [62] evaluated multiple variations of this technique and found that occupants particularly respond to feedback when it involves specific information about the energy consumption of their peers (e.g., neighbors in a dormitory building). The third category of study covers the micro-modeling of specific behaviors of occupants within their built environment. For instance, Bourgeois et al. [64] developed a sub-hourly occupancybased control model to simulate the light switching patterns of building occupants. Liao et al. [65] on the other hand developed a stochastic agent-based model to generate a time-series analysis of the location of all building occupants. The last category in the study of occupant behavior covers social, psychological, or economic drivers of behavior formation and change. Various behavioral theories exist and are commonly studied including the theory of planned behavior [66], the social identity theory [67], and the theory of normative conduct [68]. Stern [69] also proposes a comprehensive behavioral theory based on a review of various theories and models. The author classifies behavior causal drivers into: (1) personal capabilities (e.g., sociodemographic variables); (2) attitudinal factors (e.g., norms and values); (3) contextual factors (e.g., institutional and physical environment constrains); and (4) habits and routine (e.g., challenges to break existing behaviors).
A large number of studies can be found on topics related to thermal comfort [17,70–75]. Djongyang et al. [70] analyzed both rational and adaptive thermal comfort approaches, and presented a comprehensive overview of the human body thermoregulatory system. Concurrently, ASHRAE has enforced building design standards that dictate acceptable indoor conditions for occupants. These include standards ASHRAE 55-2013 [18], which covers acceptable thermal environmental conditions for human occupancy, and ASHRAE 62.1-2013 [19], defining ventilation specifications for acceptable indoor air quality. In a more recent study, Bonte et al. [36] analyzed the impact of occupants’ behavior (i.e. HVAC operation, window opening, etc.) on both energy consumption and thermal sensation. Their findings confirm the close connection between these building performance metrics and the need to evaluate them concurrently. Finally, other researchers have expanded the study of thermal comfort to outdoor environments. Examples include the work of Goshayeshi et al. [76], Monam and Rückert [77], and Djongyang et al. [70].
2.2. Human comfort
3. Materials and methods
In addition to energy conservation, occupants’ comfort and well-being is another pillar of sustainable building performance that requires careful consideration. Thermal comfort depends on the thermal tolerance of individuals to the environmental conditions they are subjected to. While there is not an absolute standard to determine whether thermal comfort feelings are acceptable or not, comfort typically occurs when body temperatures are held within narrow ranges, skin moisture is low, and the physiological effort of regulation is minimized [70]. These are highly affected by behavioral actions such as changing the thermostat setting (by occupants or facility management), altering clothing or activity levels, moving within or between environments, and opening windows.
ABM is a bottom-up simulation technique that allows for the modeling of people (e.g., occupants) as individual agents, giving them attributes, letting them interact with their environments, and observing how the macro behavior of the system emerges from the micro interactions of these agents [82]. It has been commonly used in recent years to study how the energy consumption behaviors of people can change after exposure to interventions such as energy feedback and education, or from the peer pressure generated by people sharing the same built environment (e.g., work colleagues) [37,83–85]. ABM is therefore used in this study as it allows for the capture of individual occupants’ movements, their exposure to indoor and outdoor environmental conditions defining their thermal comfort,
2.3. Human movement in urban areas and communities According to Berardi [78], there is an important cross-scale relationship between a building and its surroundings, expanding the scope of sustainable building performance from individual buildings to groups of buildings. As Weeks [79] highlights, people create an urban space, influence its performance, and are constantly influenced by it. Especially in regions with extreme climates, the movement and mobility of people become important factors to consider, and these factors depend on the design and layout of the urban area of study [80]. Furthermore, since the physical activity of pedestrians in a city is a prerequisite for sustainable health levels, the urban design of a city and the environmental conditions that encourage and support walkability also need to be examined [81]. Finally, researchers have gathered travel survey data to better understand how people move around and use their environment (e.g., [29]), with the ultimate goal of designing more efficient urban areas. In summary, sustainable building performance requires a clear understanding of the interaction of elements such as the movement patterns of people, indoor and outdoor conditions and walkability, and energy consumption levels. Despite advancements in each of these fields or areas of study, the literature still lacks a clear framework of integration to develop a comprehensive analysis of building performance. As a result, it is currently very challenging to devise strategies that simultaneously optimize the various dimensions of sustainable building performance. The next section introduces the proposed framework through a developed ‘‘agentbased” model, to be used as a proof-of-concept of the novel approach needed to bridge the aforementioned gaps in literature.
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and in turn the impact these occupants have on building energy performance. The model is developed using the platform of Anylogic, a java-based environment supporting ABM. It is detailed and presented next following the overview, design concepts, details (ODD) protocol for describing agent-based models [86,87]. 3.1. Purpose The proposed model emulates an urban environment with three main types of entities, each with its own set of attributes. The three entities are: (1) ‘‘buildings” (e.g., building type, design, and energy systems’ characteristics); (2) ‘‘occupants” (e.g., individual movement schedules, energy use characteristics, and clothing levels); and (3) ‘‘outdoor environment” (e.g., environmental conditions such as temperature and humidity). The interactions and interdependencies between these entities are then defined and simulated in order to measure key metrics, mainly energy consumption and thermal comfort levels. As detailed later, this is achieved through: (1) a ‘‘people movement” sub-model that simulates the schedules and movements of people between buildings; (2) a ‘‘thermal comfort” sub-model, which calculates occupants’ indoor and outdoor thermal comfort levels; and (3) an ‘‘energy consumption” submodel, which estimates the energy consumption of buildings. The proposed framework is general and modular, allowing its application to any group of buildings. It is later illustrated by application to a green campus environment. 3.2. Entities, state variables, and scales As stated earlier, the model has three main types of entities with specific attributes, namely ‘‘buildings”, ‘‘people”, and ‘‘outdoor environment”. Buildings constitute the indoor environment that occupants use to perform their daily activities. Each simulated building has a set of attributes that are used to calculate two main metrics, namely energy consumption and the indoor thermal comfort levels of occupants. Attributes include the type or use of each building under study (e.g., commercial, residential, classroom), which defines its operation schedule and influences its physical characteristics. Physical characteristics are design-related attributes that define: (1) building dimensions (e.g., floor area, number of floors, window-to-wall ratio (WWR), total height); (2) building envelope and construction characteristics (e.g., wall, roof, floor, and glazing characteristics such as U-values); (3) energy systems characteristics (e.g., type and size of the HVAC system as well as equipment and lighting systems specifications); and (4) other operation parameters such as thermostat set point temperatures and the energy use patterns of lighting and equipment systems. As detailed later, all of the above-mentioned characteristics are used to develop equations that predict the energy consumption of buildings for a given set of input parameters and predict the indoor thermal comfort levels of occupants. The second entity in the model is people. These are the model agents representing the occupants of the simulated buildings. Similar to buildings, each occupant has a type (e.g., worker, student, staff), which defines the buildings that this occupant will use and the schedule he/she will follow. Each agent also has some other personal attributes such as clothing level, metabolic rate, current geographic position, average moving speed, and energy use patterns. These attributes allow for the monitoring of personal characteristics of each agent in real-time, and calculate the agent’s thermal comfort levels based on the environmental conditions of his/her location in the model. For instance – and as detailed later – if an occupant is present in a specific office building at 8:30 am, the model will use the personal attributes of the occupants as well as the environmental conditions of the building to
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calculate the occupant’s indoor comfort level at this particular point in time. Finally, the ‘‘outdoor environment” defines the physical characteristics of the simulation environment. Attributes therefore include the size of simulated area (e.g., campus), the layout of buildings, and the distances that separate them. In addition, the outdoor environment also has time-dependent environmental conditions (e.g., temperature, humidity, and air speed) that are used to calculate the outdoor thermal comfort levels of occupants. For instance, if an agent is walking from a dorm to a classroom building at 8:30 am, the model would use the personal attributes of the agent (e.g., metabolic rate and clothing level) and the outdoor environmental conditions at 8:30 am to calculate his/her thermal comfort level. In summary, the three presented entities are integrated into a single modeling environment to study them in a more realistic and comprehensive manner. A process overview detailing how the model’s entities interact is presented next. 3.3. Process overview and scheduling The general execution process of the model is shown in Fig. 1, illustrating the step-by-step process the model takes from the time it is run by the user, until the end of the simulation. Prior to running the model, the user needs to enter the initial values for the model inputs, which correspond to the attributes of the buildings, people, and outdoor environment constituting the model. This is facilitated by an interface created by the authors in AnyLogic 7, which prompts the user to enter a list of parameters before running the model. An example is provided later in the application section. Once the model is run (at time t = 0), it assigns all model entities with the initial values specified by the user. The model then enters the agent loop, where sub-models 1 and 2 are applied on each agent, simulating their individual movements, and calculating their thermal comfort levels. More specifically, the ‘‘people movement” module is responsible for assigning specific schedules to occupants and triggering their movements accordingly at each time step. The ‘‘thermal comfort” module on the other hand calculates the indoor/outdoor comfort level of each occupant at each time step, given the occupant’s personal characteristics (e.g., clothing level and metabolic rate) and the environmental conditions he/she is subjected to (e.g., temperature, humidity, air speed). Once all of the agents are iterated, the model triggers sub-model 3, where the energy consumption levels of the buildings are computed and updated. The model then moves to the next time step, t = 1, where it repeats the process until the last time step is reached, completing the simulation. 3.4. Sub-models The current section details the sub-models shown in Fig. 1, which are the core of the proposed methodology. The attributes of buildings, people, and outdoor environment are first initialized based on values entered by the user. These values are highly context dependent, since each building or group of buildings can have very different characteristics. They are typically determined based on data collected from the buildings being investigated, from other case studies in literature, or from large building datasets such as the Commercial Buildings Energy Consumption Survey (CBECS) [88]. 3.4.1. Sub-model 1: people movement In this module, a schedule is assigned to each type of occupant in order to define profiles of daily routine routes. An example is
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SUB-MODEL 1 PEOPLE MOVEMENT
Buildings Inializaon
RUN
People Inializaon
Update Agent Characteriscs
SUB-MODEL 2 THERMAL COMFORT
Calculate Agent Thermal Comfort
Update Agent Posion
SUB-MODEL 3 ENERGY CONSUMPTION
Last Agent?
YES
Calculate Building Energy Consumpon
END
NO
Outdoor Environment Inializaon
Update Building Characteriscs Next Agent i++
Update Outdoor Environment Characteriscs
NO
Next Time Step t++
Last Time Step?
YES
Fig. 1. Model process overview.
STUDENTS DAILY SCHEDULE
00:00
STAFF DAILY SCHEDULE
00:00
At Dorms Off Campus 8:00 8:00 At Offices
20:00
20:00
2h aer arrival
At Offices
12:00 OR 16:00 At Classes
Fig. 2. Example of daily movement schedule of student and staff agents.
presented in Fig. 2 below, showing the schedule of two types of agents (students and staff) in the case of the university campus. In order to produce schedules and movement patterns as realistically as possible, stochasticity is added to the agents’ behaviors. Firstly, the exact time that an occupant initiates his/her movement based on a particular schedule (e.g., Fig. 2) follows a normal distribution, with a mean value of the scheduled time of action and a standard deviation of 30 min. Consequently, a randomness of around ±1 h is added to each scheduled movement. Secondly, although the same schedule is assigned for agents of similar types (e.g., staff), they do not necessarily perform the exact same sequence of movements. Instead, some agents are randomly selected to skip actions and remain at the same state until a later event during the day is triggered. In reality, this can emulate cases where an occupant decides not to follow his/her daily schedule due
to random events (e.g., sickness or the decision to go directly to class). Finally, occupants move at various speeds, simulated by assigning randomness to their walking speeds (e.g., using triangular distributions). This helps capture situations where agents might stop on their way to a destination, slightly changing their expected schedule of actions. Examples are later presented in the application section. 3.4.2. Sub-model 2: thermal comfort The predictive mean vote (PMV) model is currently one of the most widely used models to evaluate comfort. It predicts the mean response of a large group of people according to the ASHRAE thermal sensation scale, on a scale from very cold (3) to very hot (+3) [70]. The PMV calculation is shown in Eq. (1), where a value of zero is considered ideal, representing thermal neutrality. An acceptable
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comfort zone is defined as one where PMV does not exceed an absolute value of 0.5 (i.e., PMV < j0:5j).
PMV ¼ ½0:303 expð0:036 MÞ þ 0:028 L
ð1Þ
In the above, M is the total metabolic rate of work produced within the body (required for the person’s activity plus shivering) and L the thermal load on the body. L is defined in Eq. (2) as the difference between the internal heat production (i.e., metabolic rate) and heat dissipated to the environment through the surface of the skin (qsk) and the respiratory tract (qres). Both the total metabolic rate and thermal load on the human body can be expressed as a function of temperature, humidity, air speed, metabolic rate and clothing insulation. Additional details can be found in Djongyang et al. [70].
L ¼ M ðqsk qres Þ
ð2Þ
Another thermal comfort metric commonly used is the predicted percentage of dissatisfied (PPD) index, which predicts the percentage of the people who felt more than slightly warm or slightly cold for a given set of environmental conditions. PPD is directly calculated from PMV, as shown in Eq. (3).
PPD ¼ 100 95 exp½ð0:03353 PMV 4 þ 0:2179 PMV 2 Þ
ð3Þ
In this sub-model, the PPD index is calculated for each occupant, according to the above equations, and an average PPD value is estimated in annual terms for different buildings and environments (i.e., each building and the outdoor environment). 3.4.3. Sub-model 3: energy consumption Building performance simulation (BPS) is an established method used by designers and engineers to predict the energy performance of buildings. Commercial BPS software such as EnergyPlus, IES, and eQuest are commonly used for this purpose [35]. Users can input a large set of building characteristics (e.g., specifications of civil, mechanical, and electrical systems), occupancy characteristics (e.g., general building and systems’ operation schedules), and outdoor characteristics (e.g., weather conditions). The outputs of these models are detailed daily or hourly energy consumption estimations of the simulated buildings. In practice, ABM toolboxes such as Anylogic 7 do not typically support BPS capabilities. To overcome this challenge, some studies propose a real-time coupling approach where both the BPS and ABM models need to be simultaneously running and communicating. However, these studies face important limitations related to the lack of compatibility between different software packages, coding languages and protocols, as well as the challenges related to data handling and analysis [89,90]. In this paper, an alternative integration approach is used, which consists of developing regression-based surrogate models (i.e., regression-based equations) that mimic the behavior of BPS models, and integrate them into the proposed ABM framework through functions or ‘‘methods”. Several examples of regression-based training of BPS models can be found in the literature, such as the works of Amiri et al. [91] and Asadi et al. [92]. Therefore, the aim of this step is to develop regression equations that take particular parameters of interest (e.g., cooling thermostat set points, lighting and equipment intensity) as independent variables and have energy predictions (e.g., monthly building energy consumption) as dependent variables. The stepby-step surrogate training process is as follows. Firstly, BPS models are developed for the buildings under study. Secondly, different BPS input parameters related to human actions are identified (cooling thermostat set points, lighting and equipment intensity) and sampled within a pre-defined range via Latin hypercube sampling. Thirdly, using a MATLAB-EnergyPlus coupling engine, the
BPS models are automatically run using the combination of input parameters from the previous step, generating energy predictions for each run. After the parametric runs are completed, the simulated data is split between training and testing datasets (e.g., 80% and 20% of the data respectively) in order for the fitting and predictive accuracy of the regression model to be assessed. The metric used to evaluate the model’s fitting error is the adjusted R2, whereas the prediction error is measured using the mean absolute percentage error (MAPE). In summary, a regression equation is developed for each building and integrated into the ABM framework. At each time step, the equation is called to estimate the energy performance of buildings for a given set of input parameters. To illustrate this process, regression surrogate models are developed for three common types of buildings typically encountered in campus environments, including a dormitory, office, and a classroom building. An EnergyPlus BPS model is obtained for each building type from a list of models developed by the United States Department of Energy (DOE) to emulate the performance of typical or reference buildings. These models are made available for public research and have been extensively used to evaluate building codes and green labeling standards, retrofitting strategies, and new building systems and technologies. [93,94]. Given the objectives of this application, the independent variables used in the surrogate model development are human-related parameters that affect building performance, namely cooling set point temperatures, and average values of lighting and equipment intensities controlled by occupants. While other parameters (e.g., hot water usage) also affect energy consumption, they typically have a significantly lower impact on overall building performance [57,58] and are not included in this paper. Eqs. (4)–(6) illustrate the trained regressions equations for dorm, office, and classroom buildings, respectively. Eq. (7) combines the energy estimate of individual buildings to calculate that of the entire campus.
energy consumptiondorm ¼ f dorm ðx1 ; x2 ; x3 Þ ¼ 1366:9 44:527 x1 þ 50:949 x2 þ 129:08 x3 MW h
ð4Þ
energy consumptionoffice ¼ f office ðx1 ; x2 ; x3 Þ ¼ 1581:9 49:613 x1 þ 347:04 x2 þ 348:92 x3 MW h
ð5Þ
energy consumptionclass ¼ f class ðx1 ; x2 ; x3 Þ ¼ 2524:2 89:386 x1 þ 685:57 x2 þ 409:20 x3 MW h
ð6Þ
energy consumptionCAMPUS ¼ Nb: of Dorm Buildings energy consumptiondorm þ Nb: of Office Buildings energy consumptionoffice þ Nb: of Classroom Buildings energy consumptionclass
ð7Þ
In the equations above, x1 is the cooling set point temperature, x2 the average lighting intensity and x3 the average equipment intensity of the occupants for each building type. It is important to note that x2 and x3 are normalized, where a value of 1 results in energy intensities equal to those of the base case. Put differently, a value of 0.9 for x2 emulates a situation where occupants are using their lights 10% less than the base case. It is also important to highlight that using a single independent variable for each end-use
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does not limit the comprehensiveness of the regression equations in capturing micro operation patterns. As mentioned in the example above, any lighting use pattern can be converted to a normalized energy intensity factor (i.e., variable x2 ) and used in Eqs. (4)–(6). 3.5. Verification and validation A combination of verification and validation methods are used to ensure the robustness and technical validity of the model [95,96]. The general process followed is presented in Fig. 3. Firstly, conceptual validity is achieved by building the framework using validated concepts and methods from the literature. Starting with energy consumption, the developed surrogate models are based on EnergyPlus BPS models. As discussed earlier, BPS is a validated and commonly used method to predict building performance and EnergyPlus is also commonly used both in the industry and by researchers [35] (Please refer to Section 3.4.3). Similarly, thermal comfort calculations are based on the PMV and PPD models, which are again validated and used in building standard certifications [70]. The technical validity of the model is then achieved by ensuring that each of the three sub-models detailed in the previous section are implemented correctly. Starting with the energy consumption surrogate models, three metrics are specifically evaluated: goodness-of-fit, predicative accuracy, and residuals’ independence. The first is evaluated by computing the adjusted R2 of the classroom, office, and dorm surrogate models. Values above 0.99 are obtained for the three models, meaning that only 1% of the variation in the training data is not explained by the regression model. As for the predictive accuracy of the surrogate models, MAPE values between 1.8% and 2.1% are observed, confirming an excellent replication of the capabilities of the EnergyPlus BPS models. The observed results are in accordance with those of previous studies that have shown a high ability of linear regression models to replicate the energy performance of buildings. In general, this can be attributed to a strong linear relationship between various building parameters (e.g., characteristics of lighting, equipment, and HVAC systems) and energy consumption levels [92, 97–99]. Next, the Durbin-Watson test for autocorrelation [100] and the Runs test for independence [101] are conducted to ensure that the residuals are independent and uncorrelated. Beginning with the Durbin-Watson test, the tested hypotheses are: ‘‘Ho: No correlation among the residuals” and ‘‘H1: Positive or negative correlation among the residuals”. Durbin-Watson values of 2.02 (classroom building), 2.00 (dorm building) and 2.24 (office building) are obtained, where values between 1.69 and 2.32 indicate the failure to reject the null hypothesis [102], confirming the absence of correlation in the residuals. As for the Runs test for independence, the tested hypotheses are ‘‘H0: Residuals are in a random order” and ‘‘H0: Residuals are not in a random order. The obtained Run’s test p-values are 0.6318 (classroom building), 0.3457 (dorm building), and 0.6700 (office building). All p-values are greater than 0.05, hence failing to reject the null hypothesis and confirming the independence of the residuals. The technical validity of the PMV and PPD equations is achieved by randomly choosing 10 agents in the model over one simulation, and for each agent manually calculating the comfort levels given the location of the agent (e.g., environmental conditions) and his/ her personal characteristics (e.g., clothing level). As expected, the manual calculations matched the model’s calculated numbers, confirming the correct implementation of the PMV and PPD equations. Finally, 10 random agents are also selected to confirm that their movements correspond to their assigned schedules. Here again, the agents moved according to their assigned schedules, confirming the technical validity of the people movement submodel.
The last step of Fig. 3 is to ensure the operational and structural validity of the model. This step confirms that all model components and sub-models are properly operating and communicating. For instance, when calculating thermal comfort levels, the model needs to fetch in real time the environmental conditions that an agent is exposed to based on his/her location. Any delay can cause an error in the inputs of the PMV and PPD equations, resulting in inaccurate results even if the equations are implemented correctly. Two specific methods are therefore used to ensure the operational and structural validity of the model. The first method is tracing, where individual occupants are traced or observed throughout the simulation time, evaluating how the sub-models are interacting to update the characteristics and metrics of the agent (e.g., position, metabolic rate, thermal comfort) and those of the buildings (e.g., energy consumption levels). Similar to the previous steps, 10 random occupants are traced, confirming the proper operation of the sub-models. Finally, sensitivity analyses are conducted to observe the reaction of the model to changes in particular input parameters. An example is later presented in the application section where the thermostat set point temperatures are varied between two extremes while monitoring the impact on energy consumption and thermal comfort levels. For instance, it is expected that a high temperature setting (e.g., 26 °C) in a hot climate results in high PPD levels and low energy consumption levels (i.e., low cooling loads). It is important to note that the above-mentioned steps are repeated throughout the development phase and for a wide range of parameters. Finally, the authors acknowledge that due to the typical variability and uncertainty in human behavior, predictive validation of the model is extremely challenging to confirm and is not included in Fig. 3. This would require data to be collected from hundreds of buildings, which is beyond the scope of this study. To overcome this barrier, the conducted scenario analysis shown next is comparative in nature, where the authors analyze which changes to the base case model lead to the best results. In general, the approach used consists of testing which setting or strategy performs better, and by how much (e.g., percentage improvement), without necessarily predicting absolute energy and thermal comfort levels. This is a very commonly used technique in the field to overcome predictive validity limitations of human-focused models [83,84]. 4. Application 4.1. Description The proposed model is illustrated through its application to a small university campus, located in the hot and arid region of Abu Dhabi, UAE. The initialization of the model is described next, followed by a parametric variation model to help identify a HVAC operation strategy that maximizes both energy savings and thermal comfort levels (i.e., thermostat set point settings for different buildings on campus). While several strategies can be tested in the model, the choice of varying thermostat set point settings is highlighted as it illustrates the tradeoffs between energy consumption and thermal comfort, while accounting for peoples’ schedules and movements on campus. Furthermore, based on a previous work by the authors, this action is expected to show a significant impact on energy consumption, especially in extreme climates that require high cooling or heating loads [58]. 4.2. Initialization This section presents the input data values used to initialize the model and develop the business-as-usual (BAU) scenario, which is later used as a baseline for the parametric variation phase. The
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Fig. 3. Verification and validation.
input values cover the three main types of entities in the model: ‘‘buildings”, ‘‘people”, and ‘‘outdoor environment”. There is a total of 10 buildings in the model, including five midrise dormitory buildings (dorms), three mid-rise offices, and two large classroom buildings. The following indoor environmental characteristics of these buildings are assumed constant throughout the year including air speed of 0.1 m/s [18], relative humidity of 30% [103], and a baseline air temperature of 22 °C [104]. As for people initialization, the modeled campus has three main type of occupants: students, faculty and staff. A total of 500 occupants are considered, two-thirds of which are students, while the remaining are equally split between faculty and staff. The three occupancy types possess different daily schedules, which are shown in the timeline in Table 1 below. As discussed in the model description section earlier, stochasticity is applied to the people’s movement to simulate a certain level of randomness in their actions and decision-making process. Other occupancy attributes include an average moving speed assigned to each agent performing a movement. This variable follows a triangular distribution to again add a level of randomness to agent movements. The minimum value of the distribution is 0.01 m/s, the maximum value is 0.40 m/s, and the most likely value is 0.10 m/s. The relatively slow values chosen for moving speed capture potential stops that people can take on the way to their destinations (e.g., to talk to a friend or stop at a building not considered in the model). The clothing level is the same for all occu-
Table 1 Daily schedule per occupancy type. Time
Students
Faculty
Staff
8 am 9 am 10 am 12 pm 3 pm 5 pm 8 pm
Go – Go Go Go Go Go
– Go to offices Go to classrooms Go to offices Go to classrooms Leave campus –
Go to offices – – – – Leave campus –
to offices to to to to to
classrooms offices classrooms offices dorms
pants and corresponds to ‘‘light wearing” (e.g., long-sleeve shirt and trousers), as defined in ASHRAE [18]. It corresponds to 0.61 clo, where 1 clo equals 0.155 m2 K/W (0.88°F ft2 h/Btu). As for the metabolic rate, it is assumed that occupants who are indoors typically stand or relax, which according to Hoyt et al. [105] corresponds to a metabolic rate of 1.2 METS, where 1 MET equals 58.2 W/m2. On the other hand, occupants in movement are assigned a metabolic rate of 2 METS. Finally, the characteristics of the outdoors environment, including daily and hourly temperatures, humidity and air speed, are obtained from weather files of Abu Dhabi for the year 2014 [106]. 4.3. Business-as-usual The simulation results for the BAU scenario are presented in Table 2, namely baseline energy consumption and human comfort levels for a period of one year, simulated at an hourly interval. The aggregated annual energy consumption of the group of buildings considered is around 9423.8 MW h, which corresponds to an energy intensity of 232.1 kW h/m2. The classroom is the most energy-intense building type, however, office buildings contribute more to overall energy consumption since they outnumber classroom buildings (i.e., three office buildings compared to two classroom buildings). As for thermal comfort, the average PPD value on campus is 14.3% (13.9% for indoors and 43% for outdoors). Starting with the latter, the high average outdoor PPD observed is expected, given the hot weather climate of Abu Dhabi. In the case of the indoor environment, the results in all three building types are almost identical since indoor conditions – including cooling set point, air speed and humidity values – are the same. Also, occupants spend most of their time indoors, which explains why the overall PPD is mostly driven by the indoor values. Prior to proceeding with the parametric variation, it is important to highlight some of the model’s abilities that current simulation models typically lack in the literature. One example is the ability to simulate the energy performance of multiple buildings while accounting for how occupants move and share their pre-
Table 2 Annual results for the BAU scenario. Key metric
Measure
Value
Energy consumption
Campus Energy Consumption Dorm Energy Consumption (Five buildings) Offices Energy Consumption (Three buildings) Classrooms Energy Consumption (Two buildings) Average PPD – Campus Average PPD – Indoors Average PPD – Outdoors Average PPD – Dorms Average PPD – Offices Average PPD – Classrooms
Total: Total: Total: Total: 14.3% 13.9% 43.0% 14.0% 13.9% 13.9%
Thermal comfort
9423.8 MW h 2776.1 MW h 3462.7 MW h 3185.0 MW h
Average Average Average Average
per per per per
building: building: building: building:
232.1 kW h/m2 239.3 kW h/m2 229.3 kW h/m2 229.1 kW h/m2
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mises. Another example is the ability to calculate indoor and outdoor thermal comfort levels for individual occupants based on their movements and schedules, as opposed to making assumptions about space-level comfort levels. As shown next, this can help capture tradeoffs between various building performance metrics, ensuring the development of optimal building operation strategies.
4.4. Parametric variation and results A parametric variation is conducted on the set point temperatures in order to determine an optimal strategy for energy consumption and thermal comfort, while accounting for individual occupants’ movements and characteristics. For each of the three building types modeled, the set points are varied from 22 °C to 26 °C at a 1 °C increment, resulting in a total of 125 combinations or scenarios to test. For each scenario, the model is run for one simulation year calculating the energy consumption levels for all buildings and the indoor/outdoor thermal comfort levels of all occupants. Fig. 4 summarizes the findings by plotting the results of all 125 scenarios. The X, Y, and Z axes represent the temperature set point values (i.e., tested settings) for the office, dorm, and classroom buildings, respectively. The size of each sphere corresponds to the observed energy savings percentage from the BAU, whereas the color gradient reflects the thermal comfort level improvement observed, expressed in average annual PPD values. Several data trends can be observed in the results of Fig. 4. First, it is clear and expected that the lower the set points, the more energy is needed to cool the buildings, and consequently, the lower the energy savings observed. Minimal energy savings levels are in fact observed for the scenario at the bottom left side of the graph, where all set points are at 22 °C. If compared to the other extreme where the set points are programmed to 26 °C, energy saving is maximized as the building cooling loads are significantly reduced. In parallel, thermal comfort follows a different trend, where PPD, or people dissatisfaction levels in general, are high at both ends of the range (i.e., for very low or very high temperature values). PPD then gradually decreases when approaching more moderate temperatures such as 24 °C, indicating a higher thermal satisfaction of occupants. The most important trend is observed when analyzing the sensitivity of thermal comfort results for changes in thermostat set points for different building types. More specifically, for office buildings, only scenarios of 24 °C or 25 °C lead to acceptable thermal comfort levels (indicated in green1 in Fig. 4). A similar pattern is observed for variations in the set points of the dorm buildings. However, classroom buildings show a larger range of acceptable temperatures, ranging from 23 °C to 26 °C. This is largely attributed to the lower time occupants spend in that building type (please refer to Table 1), which makes the thermal comfort ‘‘penalty” from relaxing thermostat set points less important when compared to other buildings. Furthermore, occupants categorized as ‘‘staff” do not typically use classroom buildings, which reduces the impact of slightly cold (e.g., 23 °C) or slightly warm (e.g., 26 °C) temperature settings on overall campus PPD levels. Such a finding is unique to the proposed framework as the thermal comfort calculations are made while accounting for occupants’ characteristics and their movements between buildings. As discussed earlier, thermal comfort studies and models are limited to building-level evaluation and cannot capture micro occupant-level dynamics. On the other hand, the proposed framework accounts for how occupants use different buildings, which helped identify an important energy saving opportunity in classroom buildings where set points can be relaxed with 1 For interpretation of color in Fig. 4, the reader is referred to the web version of this article.
minimal thermal comfort penalty for occupants. Therefore, the results of Fig. 4 can be used in different applications. Firstly, they can serve to identify a single or set of possible solutions that satisfy particular conditions (e.g., minimal PPD levels). For instance, if the minimum PPD level is set at 6%, 16 possible solutions can be found (highlighted as green circles in Fig. 4). In other words, 16 combinations of set points exist between the classroom, office, and dorm buildings, leading to overall PPD levels of 6% or lower. Among these solutions, the one that leads to highest energy savings can be identified as the optimal strategy. From Fig. 4, this strategy corresponds to set points of 25 °C for offices and dorms and 26 °C for classrooms. Such settings have shown to reduce energy consumption by 19%, while limiting PPD to 5.8%. Secondly, the results of Fig. 4 can be used to identify threshold temperatures in building management systems. For instance, it is common in commercial buildings to limit occupants’ set point control to a certain range to avoid excessive cooling/heating loads, or uncomfortable indoor conditions. Therefore, Fig. 4 helps identify the critical temperatures to use to ensure certain energy saving or minimal PPD levels. Similarly, the identified temperatures can be used as setback settings. Put differently, when a space becomes unoccupied, the building management system can force the HVAC system to operate at a specific ‘‘setback” temperature, typically for energy saving purposes. At the same time, it is important to maintain a certain comfort level especially if the space can become occupied out of regular building operating hours (e.g., researchers going to a laboratory space in the evening). Finally, the generated results can help guide energy conservation efforts by identifying which building type should first be targeted. For instance, and as discussed earlier, the PPD levels of occupants are less sensitive to changes in the set point of classroom buildings. Therefore, an energy conservation campaign or measure that aims to increase set points temperatures on campus should start with classroom buildings. 4.5. Discussion, contributions, and limitations In summary, this paper fills an important gap in the literature by developing a novel model that integrates key metrics of sustainable building performance. The proposed framework presents important advantages over traditional modeling tools, which include the ability to: (1) model individual people, buildings, and the outdoor environment; (2) model how people move between environments; (3) calculate their indoor and outdoor thermal comfort levels, which depend on their individual movements and characteristics and those of the environment they occupy at each time step; and (4) develop robust energy predictions by importing BPS capabilities through surrogate regression models. As mentioned earlier, traditional modeling approaches lack such an integration capability, limiting them to studying each building performance metric in isolation. Beyond the direct findings, the contributions of this work to the literature are significant and various stakeholders can benefit from the study to improve the performance of their built environment. Facility managers and owners can first apply the framework to guide their energy conservation efforts. As detailed in the previous section, the framework is unique in its capability to identify tailored strategies for a multiple building environment, simultaneously maximizing various performance metrics. Similarly, policy makers can apply the framework to evaluate the performance of a larger building stock (e.g., community), understand the interactions between the buildings and their occupants, and devise strategies that optimize the overall performance of the building stock. This work also directly contributes to the field of energy modeling by proposing a unique occupant-centered approach, rather than a building-centered one. This is believed to help overcome the
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LEGEND PPD Value: 5.8% Energy Savings: 19%
5% Energy Savings 20% Energy Savings o
Office Buildings Set point: 25 C Dorm Buildings Set point: 25 oC Classroom Buildings Set point: 26 oC
10% Energy Savings 25% Energy Savings 15% Energy Savings
90th percentile
Fig. 4. Energy consumption improvement and thermal comfort levels (PPDs).
limitations of traditional energy modeling software that fail to capture different and changing attributes of building occupants. Furthermore, it expands their capabilities to model multiple-building environments that can be shared by the same occupants (e.g., campus). This research can guide the development of a future generation of building modeling software that integrates the human dimension of building performance in the energy modeling process. Finally, a broader contribution of the proposed framework is its ability to bridge the gap between various disciplines involved with the study of building performance. By adopting a human-in-theloop modeling approach, various fields can be integrated, significantly expanding the potential applications of this work. More specifically, the presented framework is highly modular and can easily be expanded to account for additional performance metrics, such as occupants’ productivity and the resulting facility life-cycle costs. This is made possible through the object-oriented programming approach of ABM, which makes expanding the attributes of agents possible without compromising the robustness nor the compactness of the modeling structure. Similarly, new attributes can be added such as the energy consumption behavior of occupants when controlling systems such as lighting and equipment, as well as the influence they can exert on each other through peer-pressure. Related work on human behavior has been previously conducted by the author [37,44,83] and can also be integrated into the current work as part of future research. Prior to concluding, it is important to mention some of the limitations of this research. Firstly, assumptions were made in regards to some of the model parameters. One such parameter is the clothing level of occupants, which was considered constant through the year, mainly due to the year-round hot climate of Abu Dhabi.
Another assumption is that thermostat set points are the same for occupied and unoccupied periods (e.g., at night), as well as weekdays and week-ends. Along the same lines, particular human actions such window opening and light switching patterns are not explicitly represented in the regression-based surrogate models (e.g., Eqs. (4)–(6)). While parameters such as X2 and X3 (i.e., average lighting and equipment intensities) can be used to account for such actions, future research can include collecting data from a large number of buildings to identify current behavioral patterns of occupants and integrate them in the proposed framework. However, and as stated earlier, the main goal of this paper was to illustrate the integration of various essential elements of building performance, an objective that was successfully achieved. Another limitation is that the developed regression-based surrogate models cannot be directly applied to other buildings with different design and characteristics. To overcome this limitation, the authors ensured that the proposed framework is generic, facilitating its customization to other buildings as well as its expansion to account for additional operation parameters (e.g., hot water use). The case study then serves as an application example. Finally, validation was limited to technical rather than predictive validity. Future research can focus on testing the model on a very large sample of buildings (e.g., at a city level) to generalize results beyond the building stock considered in this study.
5. Conclusions A novel ABM framework is proposed to comprehensively simulate human attributes and characteristics, link them to the perfor-
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mance of the built environment, and help find solutions for optimal and sustainable operation of buildings. Unlike previous work in literature, the developed model successfully integrates key dimensions of building performance, including the movement and interaction of people with their environment, building energy consumption, and thermal comfort. These capabilities are highlighted through application to a campus environment in Abu Dhabi, UAE, simulating and quantifying the tradeoff that exists between energy consumption and thermal comfort. An optimal HVAC strategy is then proposed, resulting in a 19% reduction in energy consumption and a PPD of 5.8%. Finally, the contributions of this work are significant as the proposed human-in-the-loop approach sets the ground for future research on the complex and multidisciplinary challenges encountered in the built environment. By bridging the gap between various disciplines, it successfully captures the missing ‘‘human” driver or dimension of building performance, a crucial step to designing smarter and more sustainable buildings, communities, and cities.
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling Elie Azar ⇑, Christina Nikolopoulou, Sokratis Papadopoulos Engineering Systems and Management Department, Masdar Institute, Abu Dhabi, United Arab Emirates
h i g h l i g h t s An ABM framework is proposed to optimize key building performance metrics. People movement, energy consumption and thermal comfort are efficiently integrated. The framework is based on validated concepts from literature and actual data. An application on a campus environment identifies an optimal HVAC strategy. Energy savings of 19% are observed without compromising thermal comfort levels.
a r t i c l e
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Article history: Received 26 April 2016 Received in revised form 30 August 2016 Accepted 8 September 2016
Keywords: Sustainable building performance Energy consumption Indoor and outdoor thermal comfort People movements Group of buildings Agent-based modeling (ABM)
a b s t r a c t Sustainable building performance requires the integration of various metrics such as energy consumption, thermal comfort levels, occupants’ wellbeing, and productivity. Despite their interdependence, these metrics have been mostly evaluated independently, overlooking potential tradeoffs that can occur between them (e.g., energy conservation efforts and thermal comfort). In addition, human-related factors such as occupants’ energy consumption behaviors, schedules, and movements between buildings cannot be captured using current commercial building modeling tools. Consequently, simulating the performance of a group of buildings such as in a campus, neighborhood, or city remains very challenging. In this paper, a comprehensive agent-based modeling (ABM) framework is developed to: (1) model an urban area with several buildings along with the movements and actions of people within the environment; (2) calculate key performance metrics such as indoor/outdoor thermal comfort and energy consumption levels; and (3) test and propose strategies to optimize sustainable building operation. This study illustrates the multidisciplinary approach needed to capture various dimensions of sustainable building performance. The framework is then applied to a green campus environment, identifying an energy management strategy that can reduce energy consumption by 19% without compromising occupants’ comfort and wellbeing. Ó 2016 Elsevier Ltd. All rights reserved.
1. Introduction 1.1. Background Buildings account for more than a third of the world’s total end use of energy and subsequent greenhouse gas emissions [1]. This sector typically presents the largest potential for large-scale energy savings, when compared to other sectors of the economy such as the industrial or transportation sectors [2]. This has motivated extensive research on the means to reduce the energy intensity of the built environment, which has traditionally been achieved ⇑ Corresponding author. E-mail address: [email protected] (E. Azar). http://dx.doi.org/10.1016/j.apenergy.2016.09.022 0306-2619/Ó 2016 Elsevier Ltd. All rights reserved.
through the research and promotion of energy efficient building systems and technologies, control strategies, and retrofits [3–10]. In recent years, energy conservation efforts have also expanded to include human-focused solutions or interventions such energy education, feedback, and green social marketing campaigns, which promote and incentivize energy conservation actions among building occupants [11–16]. In addition to energy conservation studies, occupants’ comfort and well-being is another building performance metric that has been studied extensively. It is estimated that on average people spend between 80 and 90 percent of their life indoors, which makes the environmental conditions of the built environment a key driver to occupants’ well-being, health, and productivity [17]. A large body of literature can therefore be found on topics such
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as indoor air quality (IAQ) and thermal comfort, as well as related building standards and regulations such as the American Society of Heating, Refrigerating and Air-Conditions Engineers’ (ASHRAE) standards 55-2013 and 62.1-2013 [4,18–21]. Previous studies in this area have largely focused on individual buildings, however, there is a recent trend in the literature to investigate the performance of a group or stock of buildings, tackling the overall sustainability challenges facing the urban environment (e.g., campus, neighborhood, or city levels) [22–26]. The increase in scope has introduced new elements or dimensions of building performance that also need to be considered. These include the movement patterns and schedules of people, outdoor environmental conditions and walkability, and urban design [22–24]. Optimizing building operations requires a comprehensive understanding and integration of the multiple performance metrics covered above [27]. Such integration is specifically important given the interdependencies and potential tradeoffs between various metrics. For instance, it has been shown that energy consumption and indoor thermal conditions are directly related and often conflict with one another [28]. Any energy conservation efforts, such as adjusting thermostat set point temperatures, should therefore ensure that occupants’ thermal comfort and productivity are not compromised. As another example, the schedule and movement of people within and between buildings (e.g., students on campus) has a direct impact on their overall or ‘‘urban” performance [29–31]. Integrating occupancy patterns in building energy performance predictions is therefore essential to develop accurate energy predictions of the buildings under investigation. 1.2. Problem statement A large body of research exists on individual metrics of building performance, however, studies that integrate these elements are still very scarce in literature. Consequently, researchers are facing important limitations to study and optimize the performance of the built environment in a holistic manner, to address its complex and multifaceted sustainability challenges [32–34]. A key contributor to the observed lack of integration is the absence of building modeling frameworks or tools capable of capturing how people use their built environment, how they affect it, and how they are affected by it. Currently, designers and engineers rely mainly on building performance simulation software (e.g., EnergyPlus, Integrated Environmental Solutions (IES), TranSys, eQuest) to model and predict the energy consumption levels of buildings [35]. Despite their robust representation of building systems and components, such simulation software present significant limitations in the modeling of occupant-related attributes and corresponding performance metrics [23,36–39]. More specifically, these tools assume common and constant attributes for all occupants, failing to distinguish between the characteristics of individual occupants (e.g., the differing schedules and energy-use operation patterns of various building systems). Furthermore, they fail to capture occupant-related performance metrics such as personal indoor and outdoor thermal comfort levels, which highly depend on the movements and interactions of people with their built environment [36–40]. 1.3. Objectives A novel agent-based modeling (ABM) framework is proposed to evaluate the performance of the built environment using a holistic and integrated human-centered approach. The framework is unique in its capability to integrate key dimensions of building performance such as the movement and scheduling of people, weather conditions, urban form (e.g., the distance between
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buildings), indoor and outdoor thermal comfort levels, and building energy consumption. The lower-level objectives of this research and the proposed framework are as follows: (1) model an urban area with several buildings along with the movements and actions of people within the environment; (2) calculate key performance metrics such as indoor/outdoor thermal comfort and energy consumption levels; and (3) test and propose building operation strategies to optimize sustainable building performance. A case study from a green university campus in Abu Dhabi, UAE, is then presented. The framework is specifically applied to simulate occupants’ movements and schedules, their indoor and outdoor thermal comfort levels, as well as the buildings’ energy consumption levels. The results helped to successfully identify an optimal heating, ventilation and air-conditioning (HVAC) strategy that maximizes both energy savings and thermal comfort levels. The contributions of this work are significant and include its ability to capture individual occupants’ characteristics and the resulting two-way interaction they can have with their environment. This helps address the lack of integration of the ‘‘human” or ‘‘individual” dimension in traditional building modeling tools and studies, setting the stage for an integrated human-in-theloop building modeling approach. Consequently, specific strategies can be identified to optimize metrics of sustainable building performance and to minimize the tradeoffs that may exist between them (e.g., energy consumption and occupants’ thermal comfort). Such strategies can be directly employed by stakeholders, including facility managers, owners, and policy makers, to improve the performance of their built environment. Finally, a broader contribution of this work is its ability to integrate various disciplines in building performance evaluation. These include – but are not limited to – building simulation and design, indoor and outdoor environmental conditions, walkability evaluation, urban planning, social and psychological aspects of energy consumption, as well as occupants’ comfort, wellbeing, and potentially productivity. Despite the growing interest of the scientific community to integrate methods and results from these fields, current literature lacks a modeling framework that allows for such an integration. The proposed framework, which is presented later, is designed to address this need, combining validated concepts and principles from various disciplines, as well as data collected from a large number of buildings. 2. Literature review Prior to proceeding with the methodology, this section summarizes literature on the main dimensions of building performance considered in this study. The dimensions include energy consumption, human comfort, and human movement in urban environments and communities. 2.1. Energy consumption and occupant behavior In the past number of decades, significant research efforts have focused on improving the efficiency and design of various buildings systems such as HVAC, lighting, office equipment, and home appliances [8–10,41–43]. A special emphasis has also been placed on building energy management and automation systems, which have shown to achieve important energy savings [3,44–47]. In parallel to research on these ‘‘active” energy saving strategies, ‘‘passive” design strategies have also been studied and developed. These mainly include building design characteristics that can passively, or indirectly, reduce energy consumption levels (e.g., building orientation, glazing type, window-to-wall ratio (WWR), window shading, wall insulation, ‘cool’ roof surface materials, and thermal mass [48–53].
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In addition to this, a human-centered energy conservation approach has also emerged in recent years, with the goal of altering current energy consumption patterns of occupants and/or facility managers [13–16,39,54–58]. The need for such an approach derives from studies directly linking building performance to how people operate and control its various systems. Consequently, any building – including a low-energy or green-certified building – runs the risk of over consuming energy if it is not operated efficiently. In general, studies on the human role in energy conservation can be divided into four categories. The first category includes studies that quantify the impact of individual human actions on building performance through observations or simulations [55–61]. For instance, Sanchez et al. [60] and Webber et al. [61] observed the energy consumption patterns of commercial building occupants and found that typically more than 50% of office equipment is left running after-hours (i.e., when the building is unoccupied). In parallel, Azar and Menassa [58] conducted sensitivity analyses on building energy models to quantify the impact of individual occupants’ actions on the energy consumption of office buildings (e.g., thermostat set point settings, lighting and equipment usage patterns). The second category tests occupancy intervention methods aiming to alter current energy consumption behaviors (e.g., energy education, feedback methods, and green social marketing campaigns) [11,13–16]. For instance, feedback methods consist of sharing building energy consumption information with occupants, as this has shown to affect current behaviors [62,63]. Peschiera et al. [62] evaluated multiple variations of this technique and found that occupants particularly respond to feedback when it involves specific information about the energy consumption of their peers (e.g., neighbors in a dormitory building). The third category of study covers the micro-modeling of specific behaviors of occupants within their built environment. For instance, Bourgeois et al. [64] developed a sub-hourly occupancybased control model to simulate the light switching patterns of building occupants. Liao et al. [65] on the other hand developed a stochastic agent-based model to generate a time-series analysis of the location of all building occupants. The last category in the study of occupant behavior covers social, psychological, or economic drivers of behavior formation and change. Various behavioral theories exist and are commonly studied including the theory of planned behavior [66], the social identity theory [67], and the theory of normative conduct [68]. Stern [69] also proposes a comprehensive behavioral theory based on a review of various theories and models. The author classifies behavior causal drivers into: (1) personal capabilities (e.g., sociodemographic variables); (2) attitudinal factors (e.g., norms and values); (3) contextual factors (e.g., institutional and physical environment constrains); and (4) habits and routine (e.g., challenges to break existing behaviors).
A large number of studies can be found on topics related to thermal comfort [17,70–75]. Djongyang et al. [70] analyzed both rational and adaptive thermal comfort approaches, and presented a comprehensive overview of the human body thermoregulatory system. Concurrently, ASHRAE has enforced building design standards that dictate acceptable indoor conditions for occupants. These include standards ASHRAE 55-2013 [18], which covers acceptable thermal environmental conditions for human occupancy, and ASHRAE 62.1-2013 [19], defining ventilation specifications for acceptable indoor air quality. In a more recent study, Bonte et al. [36] analyzed the impact of occupants’ behavior (i.e. HVAC operation, window opening, etc.) on both energy consumption and thermal sensation. Their findings confirm the close connection between these building performance metrics and the need to evaluate them concurrently. Finally, other researchers have expanded the study of thermal comfort to outdoor environments. Examples include the work of Goshayeshi et al. [76], Monam and Rückert [77], and Djongyang et al. [70].
2.2. Human comfort
3. Materials and methods
In addition to energy conservation, occupants’ comfort and well-being is another pillar of sustainable building performance that requires careful consideration. Thermal comfort depends on the thermal tolerance of individuals to the environmental conditions they are subjected to. While there is not an absolute standard to determine whether thermal comfort feelings are acceptable or not, comfort typically occurs when body temperatures are held within narrow ranges, skin moisture is low, and the physiological effort of regulation is minimized [70]. These are highly affected by behavioral actions such as changing the thermostat setting (by occupants or facility management), altering clothing or activity levels, moving within or between environments, and opening windows.
ABM is a bottom-up simulation technique that allows for the modeling of people (e.g., occupants) as individual agents, giving them attributes, letting them interact with their environments, and observing how the macro behavior of the system emerges from the micro interactions of these agents [82]. It has been commonly used in recent years to study how the energy consumption behaviors of people can change after exposure to interventions such as energy feedback and education, or from the peer pressure generated by people sharing the same built environment (e.g., work colleagues) [37,83–85]. ABM is therefore used in this study as it allows for the capture of individual occupants’ movements, their exposure to indoor and outdoor environmental conditions defining their thermal comfort,
2.3. Human movement in urban areas and communities According to Berardi [78], there is an important cross-scale relationship between a building and its surroundings, expanding the scope of sustainable building performance from individual buildings to groups of buildings. As Weeks [79] highlights, people create an urban space, influence its performance, and are constantly influenced by it. Especially in regions with extreme climates, the movement and mobility of people become important factors to consider, and these factors depend on the design and layout of the urban area of study [80]. Furthermore, since the physical activity of pedestrians in a city is a prerequisite for sustainable health levels, the urban design of a city and the environmental conditions that encourage and support walkability also need to be examined [81]. Finally, researchers have gathered travel survey data to better understand how people move around and use their environment (e.g., [29]), with the ultimate goal of designing more efficient urban areas. In summary, sustainable building performance requires a clear understanding of the interaction of elements such as the movement patterns of people, indoor and outdoor conditions and walkability, and energy consumption levels. Despite advancements in each of these fields or areas of study, the literature still lacks a clear framework of integration to develop a comprehensive analysis of building performance. As a result, it is currently very challenging to devise strategies that simultaneously optimize the various dimensions of sustainable building performance. The next section introduces the proposed framework through a developed ‘‘agentbased” model, to be used as a proof-of-concept of the novel approach needed to bridge the aforementioned gaps in literature.
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and in turn the impact these occupants have on building energy performance. The model is developed using the platform of Anylogic, a java-based environment supporting ABM. It is detailed and presented next following the overview, design concepts, details (ODD) protocol for describing agent-based models [86,87]. 3.1. Purpose The proposed model emulates an urban environment with three main types of entities, each with its own set of attributes. The three entities are: (1) ‘‘buildings” (e.g., building type, design, and energy systems’ characteristics); (2) ‘‘occupants” (e.g., individual movement schedules, energy use characteristics, and clothing levels); and (3) ‘‘outdoor environment” (e.g., environmental conditions such as temperature and humidity). The interactions and interdependencies between these entities are then defined and simulated in order to measure key metrics, mainly energy consumption and thermal comfort levels. As detailed later, this is achieved through: (1) a ‘‘people movement” sub-model that simulates the schedules and movements of people between buildings; (2) a ‘‘thermal comfort” sub-model, which calculates occupants’ indoor and outdoor thermal comfort levels; and (3) an ‘‘energy consumption” submodel, which estimates the energy consumption of buildings. The proposed framework is general and modular, allowing its application to any group of buildings. It is later illustrated by application to a green campus environment. 3.2. Entities, state variables, and scales As stated earlier, the model has three main types of entities with specific attributes, namely ‘‘buildings”, ‘‘people”, and ‘‘outdoor environment”. Buildings constitute the indoor environment that occupants use to perform their daily activities. Each simulated building has a set of attributes that are used to calculate two main metrics, namely energy consumption and the indoor thermal comfort levels of occupants. Attributes include the type or use of each building under study (e.g., commercial, residential, classroom), which defines its operation schedule and influences its physical characteristics. Physical characteristics are design-related attributes that define: (1) building dimensions (e.g., floor area, number of floors, window-to-wall ratio (WWR), total height); (2) building envelope and construction characteristics (e.g., wall, roof, floor, and glazing characteristics such as U-values); (3) energy systems characteristics (e.g., type and size of the HVAC system as well as equipment and lighting systems specifications); and (4) other operation parameters such as thermostat set point temperatures and the energy use patterns of lighting and equipment systems. As detailed later, all of the above-mentioned characteristics are used to develop equations that predict the energy consumption of buildings for a given set of input parameters and predict the indoor thermal comfort levels of occupants. The second entity in the model is people. These are the model agents representing the occupants of the simulated buildings. Similar to buildings, each occupant has a type (e.g., worker, student, staff), which defines the buildings that this occupant will use and the schedule he/she will follow. Each agent also has some other personal attributes such as clothing level, metabolic rate, current geographic position, average moving speed, and energy use patterns. These attributes allow for the monitoring of personal characteristics of each agent in real-time, and calculate the agent’s thermal comfort levels based on the environmental conditions of his/her location in the model. For instance – and as detailed later – if an occupant is present in a specific office building at 8:30 am, the model will use the personal attributes of the occupants as well as the environmental conditions of the building to
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calculate the occupant’s indoor comfort level at this particular point in time. Finally, the ‘‘outdoor environment” defines the physical characteristics of the simulation environment. Attributes therefore include the size of simulated area (e.g., campus), the layout of buildings, and the distances that separate them. In addition, the outdoor environment also has time-dependent environmental conditions (e.g., temperature, humidity, and air speed) that are used to calculate the outdoor thermal comfort levels of occupants. For instance, if an agent is walking from a dorm to a classroom building at 8:30 am, the model would use the personal attributes of the agent (e.g., metabolic rate and clothing level) and the outdoor environmental conditions at 8:30 am to calculate his/her thermal comfort level. In summary, the three presented entities are integrated into a single modeling environment to study them in a more realistic and comprehensive manner. A process overview detailing how the model’s entities interact is presented next. 3.3. Process overview and scheduling The general execution process of the model is shown in Fig. 1, illustrating the step-by-step process the model takes from the time it is run by the user, until the end of the simulation. Prior to running the model, the user needs to enter the initial values for the model inputs, which correspond to the attributes of the buildings, people, and outdoor environment constituting the model. This is facilitated by an interface created by the authors in AnyLogic 7, which prompts the user to enter a list of parameters before running the model. An example is provided later in the application section. Once the model is run (at time t = 0), it assigns all model entities with the initial values specified by the user. The model then enters the agent loop, where sub-models 1 and 2 are applied on each agent, simulating their individual movements, and calculating their thermal comfort levels. More specifically, the ‘‘people movement” module is responsible for assigning specific schedules to occupants and triggering their movements accordingly at each time step. The ‘‘thermal comfort” module on the other hand calculates the indoor/outdoor comfort level of each occupant at each time step, given the occupant’s personal characteristics (e.g., clothing level and metabolic rate) and the environmental conditions he/she is subjected to (e.g., temperature, humidity, air speed). Once all of the agents are iterated, the model triggers sub-model 3, where the energy consumption levels of the buildings are computed and updated. The model then moves to the next time step, t = 1, where it repeats the process until the last time step is reached, completing the simulation. 3.4. Sub-models The current section details the sub-models shown in Fig. 1, which are the core of the proposed methodology. The attributes of buildings, people, and outdoor environment are first initialized based on values entered by the user. These values are highly context dependent, since each building or group of buildings can have very different characteristics. They are typically determined based on data collected from the buildings being investigated, from other case studies in literature, or from large building datasets such as the Commercial Buildings Energy Consumption Survey (CBECS) [88]. 3.4.1. Sub-model 1: people movement In this module, a schedule is assigned to each type of occupant in order to define profiles of daily routine routes. An example is
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SUB-MODEL 1 PEOPLE MOVEMENT
Buildings Inializaon
RUN
People Inializaon
Update Agent Characteriscs
SUB-MODEL 2 THERMAL COMFORT
Calculate Agent Thermal Comfort
Update Agent Posion
SUB-MODEL 3 ENERGY CONSUMPTION
Last Agent?
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Outdoor Environment Inializaon
Update Building Characteriscs Next Agent i++
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Next Time Step t++
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Fig. 1. Model process overview.
STUDENTS DAILY SCHEDULE
00:00
STAFF DAILY SCHEDULE
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At Dorms Off Campus 8:00 8:00 At Offices
20:00
20:00
2h aer arrival
At Offices
12:00 OR 16:00 At Classes
Fig. 2. Example of daily movement schedule of student and staff agents.
presented in Fig. 2 below, showing the schedule of two types of agents (students and staff) in the case of the university campus. In order to produce schedules and movement patterns as realistically as possible, stochasticity is added to the agents’ behaviors. Firstly, the exact time that an occupant initiates his/her movement based on a particular schedule (e.g., Fig. 2) follows a normal distribution, with a mean value of the scheduled time of action and a standard deviation of 30 min. Consequently, a randomness of around ±1 h is added to each scheduled movement. Secondly, although the same schedule is assigned for agents of similar types (e.g., staff), they do not necessarily perform the exact same sequence of movements. Instead, some agents are randomly selected to skip actions and remain at the same state until a later event during the day is triggered. In reality, this can emulate cases where an occupant decides not to follow his/her daily schedule due
to random events (e.g., sickness or the decision to go directly to class). Finally, occupants move at various speeds, simulated by assigning randomness to their walking speeds (e.g., using triangular distributions). This helps capture situations where agents might stop on their way to a destination, slightly changing their expected schedule of actions. Examples are later presented in the application section. 3.4.2. Sub-model 2: thermal comfort The predictive mean vote (PMV) model is currently one of the most widely used models to evaluate comfort. It predicts the mean response of a large group of people according to the ASHRAE thermal sensation scale, on a scale from very cold (3) to very hot (+3) [70]. The PMV calculation is shown in Eq. (1), where a value of zero is considered ideal, representing thermal neutrality. An acceptable
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comfort zone is defined as one where PMV does not exceed an absolute value of 0.5 (i.e., PMV < j0:5j).
PMV ¼ ½0:303 expð0:036 MÞ þ 0:028 L
ð1Þ
In the above, M is the total metabolic rate of work produced within the body (required for the person’s activity plus shivering) and L the thermal load on the body. L is defined in Eq. (2) as the difference between the internal heat production (i.e., metabolic rate) and heat dissipated to the environment through the surface of the skin (qsk) and the respiratory tract (qres). Both the total metabolic rate and thermal load on the human body can be expressed as a function of temperature, humidity, air speed, metabolic rate and clothing insulation. Additional details can be found in Djongyang et al. [70].
L ¼ M ðqsk qres Þ
ð2Þ
Another thermal comfort metric commonly used is the predicted percentage of dissatisfied (PPD) index, which predicts the percentage of the people who felt more than slightly warm or slightly cold for a given set of environmental conditions. PPD is directly calculated from PMV, as shown in Eq. (3).
PPD ¼ 100 95 exp½ð0:03353 PMV 4 þ 0:2179 PMV 2 Þ
ð3Þ
In this sub-model, the PPD index is calculated for each occupant, according to the above equations, and an average PPD value is estimated in annual terms for different buildings and environments (i.e., each building and the outdoor environment). 3.4.3. Sub-model 3: energy consumption Building performance simulation (BPS) is an established method used by designers and engineers to predict the energy performance of buildings. Commercial BPS software such as EnergyPlus, IES, and eQuest are commonly used for this purpose [35]. Users can input a large set of building characteristics (e.g., specifications of civil, mechanical, and electrical systems), occupancy characteristics (e.g., general building and systems’ operation schedules), and outdoor characteristics (e.g., weather conditions). The outputs of these models are detailed daily or hourly energy consumption estimations of the simulated buildings. In practice, ABM toolboxes such as Anylogic 7 do not typically support BPS capabilities. To overcome this challenge, some studies propose a real-time coupling approach where both the BPS and ABM models need to be simultaneously running and communicating. However, these studies face important limitations related to the lack of compatibility between different software packages, coding languages and protocols, as well as the challenges related to data handling and analysis [89,90]. In this paper, an alternative integration approach is used, which consists of developing regression-based surrogate models (i.e., regression-based equations) that mimic the behavior of BPS models, and integrate them into the proposed ABM framework through functions or ‘‘methods”. Several examples of regression-based training of BPS models can be found in the literature, such as the works of Amiri et al. [91] and Asadi et al. [92]. Therefore, the aim of this step is to develop regression equations that take particular parameters of interest (e.g., cooling thermostat set points, lighting and equipment intensity) as independent variables and have energy predictions (e.g., monthly building energy consumption) as dependent variables. The stepby-step surrogate training process is as follows. Firstly, BPS models are developed for the buildings under study. Secondly, different BPS input parameters related to human actions are identified (cooling thermostat set points, lighting and equipment intensity) and sampled within a pre-defined range via Latin hypercube sampling. Thirdly, using a MATLAB-EnergyPlus coupling engine, the
BPS models are automatically run using the combination of input parameters from the previous step, generating energy predictions for each run. After the parametric runs are completed, the simulated data is split between training and testing datasets (e.g., 80% and 20% of the data respectively) in order for the fitting and predictive accuracy of the regression model to be assessed. The metric used to evaluate the model’s fitting error is the adjusted R2, whereas the prediction error is measured using the mean absolute percentage error (MAPE). In summary, a regression equation is developed for each building and integrated into the ABM framework. At each time step, the equation is called to estimate the energy performance of buildings for a given set of input parameters. To illustrate this process, regression surrogate models are developed for three common types of buildings typically encountered in campus environments, including a dormitory, office, and a classroom building. An EnergyPlus BPS model is obtained for each building type from a list of models developed by the United States Department of Energy (DOE) to emulate the performance of typical or reference buildings. These models are made available for public research and have been extensively used to evaluate building codes and green labeling standards, retrofitting strategies, and new building systems and technologies. [93,94]. Given the objectives of this application, the independent variables used in the surrogate model development are human-related parameters that affect building performance, namely cooling set point temperatures, and average values of lighting and equipment intensities controlled by occupants. While other parameters (e.g., hot water usage) also affect energy consumption, they typically have a significantly lower impact on overall building performance [57,58] and are not included in this paper. Eqs. (4)–(6) illustrate the trained regressions equations for dorm, office, and classroom buildings, respectively. Eq. (7) combines the energy estimate of individual buildings to calculate that of the entire campus.
energy consumptiondorm ¼ f dorm ðx1 ; x2 ; x3 Þ ¼ 1366:9 44:527 x1 þ 50:949 x2 þ 129:08 x3 MW h
ð4Þ
energy consumptionoffice ¼ f office ðx1 ; x2 ; x3 Þ ¼ 1581:9 49:613 x1 þ 347:04 x2 þ 348:92 x3 MW h
ð5Þ
energy consumptionclass ¼ f class ðx1 ; x2 ; x3 Þ ¼ 2524:2 89:386 x1 þ 685:57 x2 þ 409:20 x3 MW h
ð6Þ
energy consumptionCAMPUS ¼ Nb: of Dorm Buildings energy consumptiondorm þ Nb: of Office Buildings energy consumptionoffice þ Nb: of Classroom Buildings energy consumptionclass
ð7Þ
In the equations above, x1 is the cooling set point temperature, x2 the average lighting intensity and x3 the average equipment intensity of the occupants for each building type. It is important to note that x2 and x3 are normalized, where a value of 1 results in energy intensities equal to those of the base case. Put differently, a value of 0.9 for x2 emulates a situation where occupants are using their lights 10% less than the base case. It is also important to highlight that using a single independent variable for each end-use
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does not limit the comprehensiveness of the regression equations in capturing micro operation patterns. As mentioned in the example above, any lighting use pattern can be converted to a normalized energy intensity factor (i.e., variable x2 ) and used in Eqs. (4)–(6). 3.5. Verification and validation A combination of verification and validation methods are used to ensure the robustness and technical validity of the model [95,96]. The general process followed is presented in Fig. 3. Firstly, conceptual validity is achieved by building the framework using validated concepts and methods from the literature. Starting with energy consumption, the developed surrogate models are based on EnergyPlus BPS models. As discussed earlier, BPS is a validated and commonly used method to predict building performance and EnergyPlus is also commonly used both in the industry and by researchers [35] (Please refer to Section 3.4.3). Similarly, thermal comfort calculations are based on the PMV and PPD models, which are again validated and used in building standard certifications [70]. The technical validity of the model is then achieved by ensuring that each of the three sub-models detailed in the previous section are implemented correctly. Starting with the energy consumption surrogate models, three metrics are specifically evaluated: goodness-of-fit, predicative accuracy, and residuals’ independence. The first is evaluated by computing the adjusted R2 of the classroom, office, and dorm surrogate models. Values above 0.99 are obtained for the three models, meaning that only 1% of the variation in the training data is not explained by the regression model. As for the predictive accuracy of the surrogate models, MAPE values between 1.8% and 2.1% are observed, confirming an excellent replication of the capabilities of the EnergyPlus BPS models. The observed results are in accordance with those of previous studies that have shown a high ability of linear regression models to replicate the energy performance of buildings. In general, this can be attributed to a strong linear relationship between various building parameters (e.g., characteristics of lighting, equipment, and HVAC systems) and energy consumption levels [92, 97–99]. Next, the Durbin-Watson test for autocorrelation [100] and the Runs test for independence [101] are conducted to ensure that the residuals are independent and uncorrelated. Beginning with the Durbin-Watson test, the tested hypotheses are: ‘‘Ho: No correlation among the residuals” and ‘‘H1: Positive or negative correlation among the residuals”. Durbin-Watson values of 2.02 (classroom building), 2.00 (dorm building) and 2.24 (office building) are obtained, where values between 1.69 and 2.32 indicate the failure to reject the null hypothesis [102], confirming the absence of correlation in the residuals. As for the Runs test for independence, the tested hypotheses are ‘‘H0: Residuals are in a random order” and ‘‘H0: Residuals are not in a random order. The obtained Run’s test p-values are 0.6318 (classroom building), 0.3457 (dorm building), and 0.6700 (office building). All p-values are greater than 0.05, hence failing to reject the null hypothesis and confirming the independence of the residuals. The technical validity of the PMV and PPD equations is achieved by randomly choosing 10 agents in the model over one simulation, and for each agent manually calculating the comfort levels given the location of the agent (e.g., environmental conditions) and his/ her personal characteristics (e.g., clothing level). As expected, the manual calculations matched the model’s calculated numbers, confirming the correct implementation of the PMV and PPD equations. Finally, 10 random agents are also selected to confirm that their movements correspond to their assigned schedules. Here again, the agents moved according to their assigned schedules, confirming the technical validity of the people movement submodel.
The last step of Fig. 3 is to ensure the operational and structural validity of the model. This step confirms that all model components and sub-models are properly operating and communicating. For instance, when calculating thermal comfort levels, the model needs to fetch in real time the environmental conditions that an agent is exposed to based on his/her location. Any delay can cause an error in the inputs of the PMV and PPD equations, resulting in inaccurate results even if the equations are implemented correctly. Two specific methods are therefore used to ensure the operational and structural validity of the model. The first method is tracing, where individual occupants are traced or observed throughout the simulation time, evaluating how the sub-models are interacting to update the characteristics and metrics of the agent (e.g., position, metabolic rate, thermal comfort) and those of the buildings (e.g., energy consumption levels). Similar to the previous steps, 10 random occupants are traced, confirming the proper operation of the sub-models. Finally, sensitivity analyses are conducted to observe the reaction of the model to changes in particular input parameters. An example is later presented in the application section where the thermostat set point temperatures are varied between two extremes while monitoring the impact on energy consumption and thermal comfort levels. For instance, it is expected that a high temperature setting (e.g., 26 °C) in a hot climate results in high PPD levels and low energy consumption levels (i.e., low cooling loads). It is important to note that the above-mentioned steps are repeated throughout the development phase and for a wide range of parameters. Finally, the authors acknowledge that due to the typical variability and uncertainty in human behavior, predictive validation of the model is extremely challenging to confirm and is not included in Fig. 3. This would require data to be collected from hundreds of buildings, which is beyond the scope of this study. To overcome this barrier, the conducted scenario analysis shown next is comparative in nature, where the authors analyze which changes to the base case model lead to the best results. In general, the approach used consists of testing which setting or strategy performs better, and by how much (e.g., percentage improvement), without necessarily predicting absolute energy and thermal comfort levels. This is a very commonly used technique in the field to overcome predictive validity limitations of human-focused models [83,84]. 4. Application 4.1. Description The proposed model is illustrated through its application to a small university campus, located in the hot and arid region of Abu Dhabi, UAE. The initialization of the model is described next, followed by a parametric variation model to help identify a HVAC operation strategy that maximizes both energy savings and thermal comfort levels (i.e., thermostat set point settings for different buildings on campus). While several strategies can be tested in the model, the choice of varying thermostat set point settings is highlighted as it illustrates the tradeoffs between energy consumption and thermal comfort, while accounting for peoples’ schedules and movements on campus. Furthermore, based on a previous work by the authors, this action is expected to show a significant impact on energy consumption, especially in extreme climates that require high cooling or heating loads [58]. 4.2. Initialization This section presents the input data values used to initialize the model and develop the business-as-usual (BAU) scenario, which is later used as a baseline for the parametric variation phase. The
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Fig. 3. Verification and validation.
input values cover the three main types of entities in the model: ‘‘buildings”, ‘‘people”, and ‘‘outdoor environment”. There is a total of 10 buildings in the model, including five midrise dormitory buildings (dorms), three mid-rise offices, and two large classroom buildings. The following indoor environmental characteristics of these buildings are assumed constant throughout the year including air speed of 0.1 m/s [18], relative humidity of 30% [103], and a baseline air temperature of 22 °C [104]. As for people initialization, the modeled campus has three main type of occupants: students, faculty and staff. A total of 500 occupants are considered, two-thirds of which are students, while the remaining are equally split between faculty and staff. The three occupancy types possess different daily schedules, which are shown in the timeline in Table 1 below. As discussed in the model description section earlier, stochasticity is applied to the people’s movement to simulate a certain level of randomness in their actions and decision-making process. Other occupancy attributes include an average moving speed assigned to each agent performing a movement. This variable follows a triangular distribution to again add a level of randomness to agent movements. The minimum value of the distribution is 0.01 m/s, the maximum value is 0.40 m/s, and the most likely value is 0.10 m/s. The relatively slow values chosen for moving speed capture potential stops that people can take on the way to their destinations (e.g., to talk to a friend or stop at a building not considered in the model). The clothing level is the same for all occu-
Table 1 Daily schedule per occupancy type. Time
Students
Faculty
Staff
8 am 9 am 10 am 12 pm 3 pm 5 pm 8 pm
Go – Go Go Go Go Go
– Go to offices Go to classrooms Go to offices Go to classrooms Leave campus –
Go to offices – – – – Leave campus –
to offices to to to to to
classrooms offices classrooms offices dorms
pants and corresponds to ‘‘light wearing” (e.g., long-sleeve shirt and trousers), as defined in ASHRAE [18]. It corresponds to 0.61 clo, where 1 clo equals 0.155 m2 K/W (0.88°F ft2 h/Btu). As for the metabolic rate, it is assumed that occupants who are indoors typically stand or relax, which according to Hoyt et al. [105] corresponds to a metabolic rate of 1.2 METS, where 1 MET equals 58.2 W/m2. On the other hand, occupants in movement are assigned a metabolic rate of 2 METS. Finally, the characteristics of the outdoors environment, including daily and hourly temperatures, humidity and air speed, are obtained from weather files of Abu Dhabi for the year 2014 [106]. 4.3. Business-as-usual The simulation results for the BAU scenario are presented in Table 2, namely baseline energy consumption and human comfort levels for a period of one year, simulated at an hourly interval. The aggregated annual energy consumption of the group of buildings considered is around 9423.8 MW h, which corresponds to an energy intensity of 232.1 kW h/m2. The classroom is the most energy-intense building type, however, office buildings contribute more to overall energy consumption since they outnumber classroom buildings (i.e., three office buildings compared to two classroom buildings). As for thermal comfort, the average PPD value on campus is 14.3% (13.9% for indoors and 43% for outdoors). Starting with the latter, the high average outdoor PPD observed is expected, given the hot weather climate of Abu Dhabi. In the case of the indoor environment, the results in all three building types are almost identical since indoor conditions – including cooling set point, air speed and humidity values – are the same. Also, occupants spend most of their time indoors, which explains why the overall PPD is mostly driven by the indoor values. Prior to proceeding with the parametric variation, it is important to highlight some of the model’s abilities that current simulation models typically lack in the literature. One example is the ability to simulate the energy performance of multiple buildings while accounting for how occupants move and share their pre-
Table 2 Annual results for the BAU scenario. Key metric
Measure
Value
Energy consumption
Campus Energy Consumption Dorm Energy Consumption (Five buildings) Offices Energy Consumption (Three buildings) Classrooms Energy Consumption (Two buildings) Average PPD – Campus Average PPD – Indoors Average PPD – Outdoors Average PPD – Dorms Average PPD – Offices Average PPD – Classrooms
Total: Total: Total: Total: 14.3% 13.9% 43.0% 14.0% 13.9% 13.9%
Thermal comfort
9423.8 MW h 2776.1 MW h 3462.7 MW h 3185.0 MW h
Average Average Average Average
per per per per
building: building: building: building:
232.1 kW h/m2 239.3 kW h/m2 229.3 kW h/m2 229.1 kW h/m2
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mises. Another example is the ability to calculate indoor and outdoor thermal comfort levels for individual occupants based on their movements and schedules, as opposed to making assumptions about space-level comfort levels. As shown next, this can help capture tradeoffs between various building performance metrics, ensuring the development of optimal building operation strategies.
4.4. Parametric variation and results A parametric variation is conducted on the set point temperatures in order to determine an optimal strategy for energy consumption and thermal comfort, while accounting for individual occupants’ movements and characteristics. For each of the three building types modeled, the set points are varied from 22 °C to 26 °C at a 1 °C increment, resulting in a total of 125 combinations or scenarios to test. For each scenario, the model is run for one simulation year calculating the energy consumption levels for all buildings and the indoor/outdoor thermal comfort levels of all occupants. Fig. 4 summarizes the findings by plotting the results of all 125 scenarios. The X, Y, and Z axes represent the temperature set point values (i.e., tested settings) for the office, dorm, and classroom buildings, respectively. The size of each sphere corresponds to the observed energy savings percentage from the BAU, whereas the color gradient reflects the thermal comfort level improvement observed, expressed in average annual PPD values. Several data trends can be observed in the results of Fig. 4. First, it is clear and expected that the lower the set points, the more energy is needed to cool the buildings, and consequently, the lower the energy savings observed. Minimal energy savings levels are in fact observed for the scenario at the bottom left side of the graph, where all set points are at 22 °C. If compared to the other extreme where the set points are programmed to 26 °C, energy saving is maximized as the building cooling loads are significantly reduced. In parallel, thermal comfort follows a different trend, where PPD, or people dissatisfaction levels in general, are high at both ends of the range (i.e., for very low or very high temperature values). PPD then gradually decreases when approaching more moderate temperatures such as 24 °C, indicating a higher thermal satisfaction of occupants. The most important trend is observed when analyzing the sensitivity of thermal comfort results for changes in thermostat set points for different building types. More specifically, for office buildings, only scenarios of 24 °C or 25 °C lead to acceptable thermal comfort levels (indicated in green1 in Fig. 4). A similar pattern is observed for variations in the set points of the dorm buildings. However, classroom buildings show a larger range of acceptable temperatures, ranging from 23 °C to 26 °C. This is largely attributed to the lower time occupants spend in that building type (please refer to Table 1), which makes the thermal comfort ‘‘penalty” from relaxing thermostat set points less important when compared to other buildings. Furthermore, occupants categorized as ‘‘staff” do not typically use classroom buildings, which reduces the impact of slightly cold (e.g., 23 °C) or slightly warm (e.g., 26 °C) temperature settings on overall campus PPD levels. Such a finding is unique to the proposed framework as the thermal comfort calculations are made while accounting for occupants’ characteristics and their movements between buildings. As discussed earlier, thermal comfort studies and models are limited to building-level evaluation and cannot capture micro occupant-level dynamics. On the other hand, the proposed framework accounts for how occupants use different buildings, which helped identify an important energy saving opportunity in classroom buildings where set points can be relaxed with 1 For interpretation of color in Fig. 4, the reader is referred to the web version of this article.
minimal thermal comfort penalty for occupants. Therefore, the results of Fig. 4 can be used in different applications. Firstly, they can serve to identify a single or set of possible solutions that satisfy particular conditions (e.g., minimal PPD levels). For instance, if the minimum PPD level is set at 6%, 16 possible solutions can be found (highlighted as green circles in Fig. 4). In other words, 16 combinations of set points exist between the classroom, office, and dorm buildings, leading to overall PPD levels of 6% or lower. Among these solutions, the one that leads to highest energy savings can be identified as the optimal strategy. From Fig. 4, this strategy corresponds to set points of 25 °C for offices and dorms and 26 °C for classrooms. Such settings have shown to reduce energy consumption by 19%, while limiting PPD to 5.8%. Secondly, the results of Fig. 4 can be used to identify threshold temperatures in building management systems. For instance, it is common in commercial buildings to limit occupants’ set point control to a certain range to avoid excessive cooling/heating loads, or uncomfortable indoor conditions. Therefore, Fig. 4 helps identify the critical temperatures to use to ensure certain energy saving or minimal PPD levels. Similarly, the identified temperatures can be used as setback settings. Put differently, when a space becomes unoccupied, the building management system can force the HVAC system to operate at a specific ‘‘setback” temperature, typically for energy saving purposes. At the same time, it is important to maintain a certain comfort level especially if the space can become occupied out of regular building operating hours (e.g., researchers going to a laboratory space in the evening). Finally, the generated results can help guide energy conservation efforts by identifying which building type should first be targeted. For instance, and as discussed earlier, the PPD levels of occupants are less sensitive to changes in the set point of classroom buildings. Therefore, an energy conservation campaign or measure that aims to increase set points temperatures on campus should start with classroom buildings. 4.5. Discussion, contributions, and limitations In summary, this paper fills an important gap in the literature by developing a novel model that integrates key metrics of sustainable building performance. The proposed framework presents important advantages over traditional modeling tools, which include the ability to: (1) model individual people, buildings, and the outdoor environment; (2) model how people move between environments; (3) calculate their indoor and outdoor thermal comfort levels, which depend on their individual movements and characteristics and those of the environment they occupy at each time step; and (4) develop robust energy predictions by importing BPS capabilities through surrogate regression models. As mentioned earlier, traditional modeling approaches lack such an integration capability, limiting them to studying each building performance metric in isolation. Beyond the direct findings, the contributions of this work to the literature are significant and various stakeholders can benefit from the study to improve the performance of their built environment. Facility managers and owners can first apply the framework to guide their energy conservation efforts. As detailed in the previous section, the framework is unique in its capability to identify tailored strategies for a multiple building environment, simultaneously maximizing various performance metrics. Similarly, policy makers can apply the framework to evaluate the performance of a larger building stock (e.g., community), understand the interactions between the buildings and their occupants, and devise strategies that optimize the overall performance of the building stock. This work also directly contributes to the field of energy modeling by proposing a unique occupant-centered approach, rather than a building-centered one. This is believed to help overcome the
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LEGEND PPD Value: 5.8% Energy Savings: 19%
5% Energy Savings 20% Energy Savings o
Office Buildings Set point: 25 C Dorm Buildings Set point: 25 oC Classroom Buildings Set point: 26 oC
10% Energy Savings 25% Energy Savings 15% Energy Savings
90th percentile
Fig. 4. Energy consumption improvement and thermal comfort levels (PPDs).
limitations of traditional energy modeling software that fail to capture different and changing attributes of building occupants. Furthermore, it expands their capabilities to model multiple-building environments that can be shared by the same occupants (e.g., campus). This research can guide the development of a future generation of building modeling software that integrates the human dimension of building performance in the energy modeling process. Finally, a broader contribution of the proposed framework is its ability to bridge the gap between various disciplines involved with the study of building performance. By adopting a human-in-theloop modeling approach, various fields can be integrated, significantly expanding the potential applications of this work. More specifically, the presented framework is highly modular and can easily be expanded to account for additional performance metrics, such as occupants’ productivity and the resulting facility life-cycle costs. This is made possible through the object-oriented programming approach of ABM, which makes expanding the attributes of agents possible without compromising the robustness nor the compactness of the modeling structure. Similarly, new attributes can be added such as the energy consumption behavior of occupants when controlling systems such as lighting and equipment, as well as the influence they can exert on each other through peer-pressure. Related work on human behavior has been previously conducted by the author [37,44,83] and can also be integrated into the current work as part of future research. Prior to concluding, it is important to mention some of the limitations of this research. Firstly, assumptions were made in regards to some of the model parameters. One such parameter is the clothing level of occupants, which was considered constant through the year, mainly due to the year-round hot climate of Abu Dhabi.
Another assumption is that thermostat set points are the same for occupied and unoccupied periods (e.g., at night), as well as weekdays and week-ends. Along the same lines, particular human actions such window opening and light switching patterns are not explicitly represented in the regression-based surrogate models (e.g., Eqs. (4)–(6)). While parameters such as X2 and X3 (i.e., average lighting and equipment intensities) can be used to account for such actions, future research can include collecting data from a large number of buildings to identify current behavioral patterns of occupants and integrate them in the proposed framework. However, and as stated earlier, the main goal of this paper was to illustrate the integration of various essential elements of building performance, an objective that was successfully achieved. Another limitation is that the developed regression-based surrogate models cannot be directly applied to other buildings with different design and characteristics. To overcome this limitation, the authors ensured that the proposed framework is generic, facilitating its customization to other buildings as well as its expansion to account for additional operation parameters (e.g., hot water use). The case study then serves as an application example. Finally, validation was limited to technical rather than predictive validity. Future research can focus on testing the model on a very large sample of buildings (e.g., at a city level) to generalize results beyond the building stock considered in this study.
5. Conclusions A novel ABM framework is proposed to comprehensively simulate human attributes and characteristics, link them to the perfor-
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mance of the built environment, and help find solutions for optimal and sustainable operation of buildings. Unlike previous work in literature, the developed model successfully integrates key dimensions of building performance, including the movement and interaction of people with their environment, building energy consumption, and thermal comfort. These capabilities are highlighted through application to a campus environment in Abu Dhabi, UAE, simulating and quantifying the tradeoff that exists between energy consumption and thermal comfort. An optimal HVAC strategy is then proposed, resulting in a 19% reduction in energy consumption and a PPD of 5.8%. Finally, the contributions of this work are significant as the proposed human-in-the-loop approach sets the ground for future research on the complex and multidisciplinary challenges encountered in the built environment. By bridging the gap between various disciplines, it successfully captures the missing ‘‘human” driver or dimension of building performance, a crucial step to designing smarter and more sustainable buildings, communities, and cities.
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