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Importance of occupancy information when simulating energy demand of energy efficient house: A case study
Energy and Buildings 101 (2015) 64–75
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Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Importance of occupancy information when simulating energy demand of energy efficient house: A case study Vytautas Martinaitis a , Edmundas Kazimieras Zavadskas b,∗ , Violeta Motuziene˙ a , Tatjana Vilutiene˙ b a
Department of Building Energetics, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio ave. 11, LT-10223 Vilnius, Lithuania Department of Construction Technology and Management, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, Sauletekio ave. 11, LT-10223 Vilnius, Lithuania b
a r t i c l e
i n f o
Article history: Received 19 December 2014 Received in revised form 17 April 2015 Accepted 18 April 2015 Available online 25 April 2015 Keywords: Occupancy profile 3D model Energy efficient house Automated building energy systems Overheating Comfort strategy Auxilary energy
a b s t r a c t During planning and design stages of energy efficient houses investor and designer deal with requirement to ensure very low energy consumption in operational stage of building life cycle. Any deviation appeared after building commissioning will result non-compliance with regulatory requirements. Currently, the energy simulations of the residential building do not provide the approach, which would help comprehensively evaluate differences of occupants’ characteristics (age, behaviour, etc.), neither in energy performance certification methodologies nor in energy simulation software. The objective of study is to perform complex analysis of the effect of domestic occupancy profiles on the energy performance of energy efficient house and assess applicability of default DesignBuilder occupancy profiles at local conditions. Results show that the influence of the profile with 4 occupants (OP2) on overall primary energy demand for different cases is never exceeding 5% compared to default DesignBuilder profiles. The influence of households with 2 occupants (OP3, OP4) on overall primary energy demand varies from 14% to 21% compared to default profiles. The simulation results revealed that the use of different occupancy profiles correlates with the total energy performance of the building. Incorporation of occupancy information in energy simulations will improve the accuracy of the energy performance assessment. © 2015 Elsevier B.V. All rights reserved.
1. Introduction The negative environmental impacts of rapidly growing world energy consumption as well as vision of energy efficient sustainable buildings was discussed in the last decade [1–4] pointing that a successful creation of sustainable infrastructure systems, energy efficient and environmentally-conscious designs requires a holistic, integrated, and multidisciplinary approach. Unfortunately, because of the large number and diversity of participants, construction work is characterised by a fragmented decision-making process, fear of venture, and conservatism, which result in innovation avoidance and missed opportunities. In the processes of building design, operation and maintenance, the technological, institutional and cultural cooperation of architects, engineers and constructors is very important [5]. Due to the rising awareness of climate change and rise of
∗ Corresponding author. Tel.: +370 69820779. E-mail addresses: [email protected] (V. Martinaitis), [email protected], [email protected] (E.K. Zavadskas), ˙ [email protected] (T. Vilutiene). ˙ [email protected] (V. Motuziene), http://dx.doi.org/10.1016/j.enbuild.2015.04.031 0378-7788/© 2015 Elsevier B.V. All rights reserved.
challenging requirements for energy performance of buildings, the energy efficiency of buildings has to be estimated at an early design stage. Solutions made at early design stage determine building’s environmental performance during its entire life cycle. In order to evaluate the dependencies of performance criteria of form, material and technical systems, building performance assessment has to be integrated into the design process [6]. Computational analysis enables more precise and fast evaluation of building design alternatives, therefore researchers encourage using simulation tools throughout the whole building design process [7]. Most popular expert tools use physical calculation models for the calculation such as TRNSYS [8], IES Virtual Environment [9] or EnergyPlus [10]. Physical calculation models make the precise calculation of detailed tasks as well as overall energy consumption possible. From zone loads, daylighting and solar to multizone airflow, highly precise calculations for every possible engineering task are available. However, interactions between different simulation tools are limited, thus designer could meet difficulties in conducting fast and comprehensive building performance assessment. Many debates appear on rethinking and reassessing of what issues could be potentially addressed to allow the building
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simulation tools to be used in more efficient way throughout the whole building design process. Although there are many studies on prediction of energy performance in buildings, the resolution of simulation input information regarding occupancy (i.e. people’s presence and behaviour in buildings) is still rather low [11]. The aim of the article is to perform complex analysis of the effect of typical domestic occupancy profiles on the energy performance of energy efficient house and assess applicability of default DesignBuilder (DB) occupancy profiles at local conditions. In the previous study published [12], domestic hot water energy and summer comfort was not considered, therefore this study is extended to the analysis of energy demand required to ensure appropriate summer comfort in the building with the use of solar shading and alternative ventilation strategy. The paper also presents the framework for integrated building design that involves the analysis of the energy-related impacts of all building components, including the building location, envelope, heating, ventilation and air conditioning (HVAC), domestic hot water (DHW), lighting, controls, and equipment as well as the impact of occupancy characteristics and the application of simulation tool EnergyPlus (DesignBuilder). 2. Literature review 2.1. Integrated building design An integrated or whole building design process involves studies of the energy-related impacts and interactions of all building components, including the building location, envelope (walls, windows, doors, and roof), heating, HVAC, DHW, lighting, controls, and equipment [13–17] as well as the impact of occupancy characteristics [18–22]. Table 1 presents the examples of recent studies on application of new techniques and integrated building design process. 2.2. Rebound effect Buildings usually do not perform as predicted, even when very sophisticated energy simulation methods are used [23–27]. One of the reasons of such discrepancy between theoretical models and real building performance is the influence of human behaviour and the preferences of occupants. People affect the performance of buildings, due to their presence (passive effects) and their actions (active effects) [11] (Fig. 1). Energy and thermal performance of buildings is affected by people’s presence not just as a source of heat and water vapour, but also due to their actions, such as, operation of appliances, manipulation of building control devices for heating, cooling, ventilation and lighting, DHW consumption. Expected energy conservation cannot be fully achieved because the occupants might demand for more comfortable lifestyles with more
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energy services in buildings as building energy efficiency improves [27]. Scientists call this effect a ‘performance gap’ or ‘rebound effect’. The study [27] revealed that the rebound effect in the rural residential buildings is much larger than that in the urban residential buildings, this leads to the conclusion that one-flat residential buildings require more attention for energy simulation on design stage. 2.3. Energy consumption patterns For the prediction of energy consumption in building the energy consumption pattern in exact building has to be known. For modelling of building occupants’ passive and active effects, specialists usually rely on default load schedules of occupancy given in libraries of simulation tools. Occupancy profiles, heat load profiles as well as electricity load profiles in office buildings can be correctly predicted, whereas the absence of people in building and necessity for required comfort is known [19]. However, the prediction of energy consumption patterns in residential buildings is more complicated, especially in single family houses, since the diverse behaviour of households depend on many factors, such as: price of energy, users awareness of energy issues, gender, age, employment, family size, socio-cultural belonging, etc. [28–32]. The data on HVAC systems, electricity and domestic hot water systems, as well as energy consumption profiles is needed. The assumption made on activities that people perform when they are at home and awake enable to estimate indirectly the electricity consumption in buildings [33]. The occupancy pattern also depends on seasonality. Torriti [33] has explored electricity demand peaks in residential buildings concerning the time of active occupancy of single-person households in 15 European countries. The assessment of national variance in occupancy levels was made at the aggregate European level using Hetus (Harmonised European Time Use Survey) [34] statistical data of people’s use of time in different European countries. Analysis revealed that the differences in time spent for different activities depend on culture and climate zone. Study of Santamouris et al. [35] revealed that even the serious economic situation in the country has influence on the energy consumption and environmental quality of households. Results show that during the economic crisis in Greece indoor temperatures during the winter season 2012–2013 were much below the accepted standards and the energy consumption for heating was found to be minimum and much below the country’s threshold while a high fraction of households was not using heating energy at all. The specific study of Santamouris [36] makes clear that urban warming has also a very significant impact on the global energy consumption of buildings. The effect of occupants’ behaviour on building energy consumption was examined in many studies [37–41]. Recent studies analyse different factors related to human behaviour, for example, family size, the control mode of the heating system and management of the heated area [38]. Although
Table 1 Sample publications on application of new techniques and integrated building design process. Reference
Main topic
Schlueter and Thesseling [6] Bleil de Souza [7] Bleil de Souza [13] Larsen et al. [14] Ochoa and Capeluto [15] Petersen and Svendsen [16] Kim et al. [17] Korjenic and Bednar [18] Azar and Menassa [19] Oldewurtel et al. [20] de Meestera et al. [21] Saelens et al. [22]
Building information model based energy/exergy performance assessment in early design stages Debate on building thermal simulation tools to be better used throughout the whole building design process Study on the use of building thermal physics to inform design decision making Integrating simulation tools in the design of energy-saving buildings Early design stages of intelligent facades Simulation program informed decisions in the early stages of building design Analysis of an energy efficient building design Influence of building occupancy patterns and activities on total energy performance of building The impact of occupancy parameters in energy simulation of office buildings Importance of occupancy information for building climate control Impacts of occupant behaviours on residential heating consumption Energy and comfort performance of building systems including occupant behaviour
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Fig. 1. Active and passive occupants’ effects on building performance.
in the last decade a number of studies on the occupants’ behaviour impact have been carried out there is still need for highly transparent research designs and clearly defined statistics to evaluate the predictive performance of an occupancy model [37]. Summarizing the analysis, it could be concluded that in different countries inhabitants behave differently. Predicting the building energy demand, this fact has to be taken into account already at the early design stage.
important. Simulation tools considered as efficient instrument for increasing the total energy performance of building systems. Numerical or analytical integrated simulation techniques are the base of simulation tools. Integrated simulation methods can be divided into two groups: analytical and numerical. Clarke [43] and Underwood and Yik [46] describe them in details. Despite the fact that both of these groups have their advantages and disadvantages, both are suitable for the assessment of building energy performance.
2.4. Methods for assessing energy consumption in buildings 3. Case study In common engineering practice simplified building energy demand calculation methods prevail. Usually construction elements and engineering systems are evaluated separately, and human behaviour is assessed only minimally. According to ISO standard “Energy performance of buildings—Calculation of energy use for space heating and cooling” [42], energy demand calculations can be carried out using three alternative calculation methods: semi-stationary, simplified hourly dynamic and detailed dynamic methods. The building should be seen as a complex, dynamic and nonlinear system where parameters change at different speeds, and they depend on the thermodynamic state [43]. Therefore, there are doubts if simplified methods used in the engineering practice are always capable to evaluate complex systems, and the use of computer simulation programs is required. Incorporation of computational methods in architecture makes building performance analysis possible at the early design stage. Viewing the building as a system and revealing complex dependencies makes possible new approaches. Successful application of different simulation tools to minimise efficiently the overall energy consumption of the described in [6,18,44,45]. The results clearly illustrate that in order to reach the energy goals with economic efficiency the use of simulation tools at early design stage is very
A study presented in this paper has been carried out in the frame of the project “Building and Renewable Energy Sustainability Model” (hereinafter—the project) funded by the Research Council of Lithuania is presented in this paper. One of the aims of the project is to investigate energy demand of low energy buildings. This objective corresponds to the European Union’s targets for 2020: deployment of nearly zero energy buildings. Architectural and engineering solutions of buildings must be adapted according to the climatic conditions. The goal of the project is to develop and test a model for the assessment of the energy, exergy (thermodynamic) and ecological (environmental impact during the whole life cycle) efficiency of building energy systems (heating, ventilation, cooling, hot water systems, lighting) using renewable energy. The study is based on the assumption that the highest energy efficiency potential is on end-user side. In most cases, building energy systems function independently of each other. Interactions between different energy systems are not taken into account; therefore to optimise the energy consumption an integrated analysis of building and different building energy systems is required. The simulation engine EnergyPlus [10] has been used to simulate building’s annual energy demand with step of 1 h in this
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Fig. 2. Conceptual framework of main energy efficient building planning and design stages.
Fig. 3. Iterations in simulation steps.
study. The DesignBuilder, which is an interface of simulation engine EnergyPlus, was used to create the 3D building model. Simulation relies on successive substitution iteration to reconcile supply and demand using the Gauss–Seidell philosophy of continuous updating [12]. Fig. 2 presents the conceptual framework of main energy efficient building planning and design stages proposed by authors. This paper presents the analysis performed on stage 3 (simulation of building energy systems) of energy efficient building planning and design process (see Fig. 2). The analysis performed of other stages presented in [5,47,48]. The analysis performed includes following steps with iterations (Fig. 3):
5. Shading options were selected and simulated taking into account two criteria: minimal influence on heating demand and maximum influence on summer comfort. Together with shading, ventilation was improved. 6. To improve summer comfort alternative ventilation with free cooling was applied. 7. Analysis of influence of occupancy profiles on primary energy demand was performed in all of the above-mentioned steps. 8. Sensitivity analysis for different cool climate locations was performed.
1. Creation of model of the energy efficient building using default residential occupancy and systems operation profiles, supplied by the software library. 2. Creation and simulation of 3 alternative typical occupancy, lighting, DHW and HVAC system management schedules. 3. Creation and simulation of 4 different heating system management scenarios for 4 occupancy profiles (16 cases). 4. Improvements of lighting system for all cases were performed and simulated. Most energy efficient winter comfort strategy was identified for further summer comfort analysis.
3.1.1. Building geometry and structural elements Object of the analysis is one-storey, quadrate shape house (see Fig. 4, 3D model view and plan view in DesignBuilder), total area is 81 m2 . Several assumptions were made in creating the model:
3.1. Building description
• house occupation is standard family consisting of 4 persons (parents and 2 children); • appropriate thermal and lighting comfort corresponding to the requirements of national norms has to be maintained during the occupation hours;
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Fig. 4. Plan (a) and south fac¸ade (b) of the analysed building 3D model in DesignBuilder.
• the building is compact (small surface-area-to-volume ratio); • plan and geometry of the house created as a cell for developing a multi-storey residential building; and • building is planned to be energy efficient.
Structural elements of the building envelope were selected according to the requirements fixed in the national standard STR 2.01.09:2012 [50] for the buildings having energy performance label A+. Economical availability of construction materials was also
V. Martinaitis et al. / Energy and Buildings 101 (2015) 64–75 Table 2 U-values of the building envelope. Element
U-value (W/(m2 K)
Exterior wall Floor on the ground Roof Interior partitions Glazing Window frames
0.11 0.15 0.11 1.49 0.77 0.90
Table 3 Internal required temperatures (according to HN 42:2009 [54]). Room
Standard required temperature (◦ C)
Kitchen; bedroom 1, 2; lounge (living room) Bathroom Hall (circulation area)
18–22 20–23 14–16
taken into account. Sizes of windows in differently oriented facades were selected taking into account the requirements of the national standard STR 2.02.09:2005 [52] and recommendations given in [51], i.e. size of windows is minimal required to satisfy daylighting needs of the occupants and to minimize risk of overheating in summer and significant heat loss in winter. Thermal properties of the envelope of analysed house presented in Table 2.
3.1.2. Weather data The typical year data files were taken from the IWEC [49]. Typical meteorological weather data used for simulation represents Kaunas city (Lithuania). Temperatures for heating 19.3 ◦ C and for cooling 27.5 ◦ C.
3.1.3. Base case lighting It was assumed that lighting system operates during occupancy hours, when people are active. Lighting levels correspond to the national regulations [53]: bathroom—75 lx, bedrooms—200 lx, living room and kitchen—300 lx, hall—100 lx. Assumed specific power of illuminating devices is 3 W/m2 /100 lx. For the base case no lighting control was assumed.
3.1.4. Base case comfort Internal temperatures are set according to requirements of the standard [54] (see Table 3). Assuming that high comfort level is desired during the occupation hours, in the base case the highest allowed temperature is maintained. Since in reality occupants tend to heat their homes just at time of presence [40], therefore in the simulated house comfort temperature is maintained just during occupation hours adding 1 h before occupants come back home. For the base case, 3 ◦ C heating set back temperature (temperature that is maintained when rooms are unoccupied) is applied (except hall, where temperature is very low). Such temperature reduction has insignificant influence on comfort and moisture problems and leads to savings.
3.1.5. Base case ventilation Building has mechanical ventilation system with heat recovery. Fresh air flows parameters correspond to the requirements of national standards. Fresh air is supplied only during occupation hours and additional 1 h before occupants come back home. During unoccupied hours system operates in recirculation mode. The total average fresh air change rate in the building is 0.6 h−1 (includes infiltration and mechanical ventilation). This air change rate will be kept constant in all analysed below occupancy schedules, since supplied air flows were selected according to the area of the building, but not number of occupants.
3.1.6. Base case and alternative occupancy profiles Building occupancy profiles (schedules) describe how building and its systems operate and used to determine the character of influence on total energy consumption of the building. Occupancy is described separately for working days, weekends and holidays, i.e. schedules vary during the year. For the base case default occupancy schedules existent in the simulation software were assumed. Since occupancy profiles of residential building proposed by default in EnergyPlus software package do not correspond to the life style of northern Europe and they do not reflect the most common representative household composition and activities, more realistic alternatives were simulated and compared (see Table 4). Daily occupancy patterns for cases OP2–OP4 were created according to [38] and Hetus [34] data as well as taking into account inhabits of the Lithuanian occupants. Of course, there might also be other possible characteristics of households, but this study analyses only the most typical ones. The alternative occupancy profiles for the circulation area were not analysed since no data except DesignBuilder was found for the occupancy of this zone. Maximum occupation (when occupancy fraction is equal to 1) was assumed for premises: bedroom no. 1 (in case of OP1, OP2—bedroom of children, OP3—office, OP4—spare bedroom), (2) bedroom no. 2–2, kitchen—2 or 4, living room—2 or 4, bathroom—1. Daily occupancy profiles are expressed as fraction from the maximum occupation (Fig. 5). Differences of occupants’ behaviour during the year were not taken into account.
3.1.7. Base case and alternative heating strategies Comfort, as a rule, is understood by occupants differently. Even if we know occupation profiles quite well, it is difficult to predict their comfort preferences before building starts its operation. Therefore influence of the thermostat management (heating strategy) on energy demand of the building was analysed in combination with different occupation profiles. Properly using a programmable thermostat is one of the easiest ways to save energy without sacrificing comfort and sticking heating system’s settings to the occupant’s schedule. Four different thermal comfort strategies in all occupation schedules were analysed. COM1 and COM2 are strategies when high internal temperature is maintained during the occupation hours, but with different setback temperatures, COM3 strategy assumes the average temperatures during the occupied hours with temperature setback, and COM4 strategy the same as COM3, but without temperature setback (Table 5).
Table 4 Description of alternative occupancy profiles. Occupancy profile
Household characteristics
OP1 OP2
Standard residential occupancy profile for different zones of the building, household consists of 4 persons Household consists of 4 persons (parents are actively working, 2 children stay at school or kindergarten during the working hours; children come back home together with parents) Household consists of 2 persons (actively working) Household consists of 2 persons (retired couple)
OP3 OP4
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Fig. 5. Analysed daily occupancy profiles of building model: (a) alternative OP1; (b) alternative OP2; (c) alternative OP3; (d) alternative OP4.
Table 5 Analysed occupation strategies. Occupation strategies
COM1 COM2 COM3 COM4
Assumed set/setback temperatures (◦ C) Kitchen Bedroom 1, 2 Lounge
Bathroom
22/19 22/18 20/18 20
23/20 23/19 21/19 21
3.1.8. Improved summer comfort strategies It is well known that in new tight and highly insulated buildings the risk of overheating is likely to increase. The above described building with base case HVAC and lighting systems also has faced overheating problem for all of the occupancy scenarios analysed. Summer design temperature is not met 1916–2823 h a year depending on the occupancy profile and temperatures are peaking at 36 ◦ C. Most convenient response to a problem of overheating is to invest in a local active cooling system, but it will undo many of the reductions in energy consumption and carbon dioxide emissions achieved by the improvements made to building. Furthermore, it is certainly not a sustainable solution considering the climate in Lithuania, where the risk of overheating can be addressed effectively by passive design measures.
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In order to solve overheating problem, two measures were analysed: solar shading and alternative ventilation strategy (decreased recirculated air flow) with free cooling option (using economiser). An economiser is a damper opening that draws up to 100% outside air when the outside air is cooler than the temperature inside the building, thereby providing free cooling. Recirculated air flow in the constant volume mechanical ventilation system was also decreased twice to minimise energy demand of fans. Different internal and external (static and movable) shading alternatives were considered for the building. Shading makes positive effect on summer comfort of the house, but it also more or less makes negative effect on the heating energy demand. Therefore final decision choosing shading was made considering two criteria: minimum effect on heating energy demand and maximum effect on overheating hours. External blinds, operating just during non-heating season and operated according to the inside air temperature, were selected.
4. Simulation results 4.1. Influence of occupancy profiles, heating strategies and lighting control Four different occupancy profiles were simulated for base case comfort strategy during the period of one year using the time step of 1 h (Fig. 6). Results of simulation (Fig. 7) show that, if different occupancy profiles are considered for the same building, significant differences in energy consumption may be expected. Vivid differences appear in occupational strategies were household composed with less than four persons. The results for occupancy profile OP3 (only two adult occupants) for the base case show the increase of 17% for heating and 6% for electricity consumed for lighting, and the reduction of 2% for auxiliary equipment compared with the case OP2 (see Fig. 7a–c). Moreover, in the case with retired couple (OP4), results show a 30% increase in energy consumption for heating, 4% increase in electricity consumption for auxiliary equipment and 16% rise
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in energy consumption for lighting compared with the case OP2. These increases related to longer operation of building systems. Looking at the total primary energy consumption (Fig. 7d) (primary energy factor applied for electricity—2.8) of the building, it is obvious that house with occupancy profile OP1 (default DB profile) is the least energy efficient and OP3—the most efficient one. Since DHW is an important energy using system of residential building, number of occupants makes a significant influence on the overall energy performance of the building. Therefore, taking into account DHW primary energy demand causes obvious differences in performance of the building with two and four occupants. At the same time results for realistic occupancy profiles of Eastern–Northern European family (OP2) show relatively small differences comparing with the default profile (OP1) available in the simulation tool DesignBuilder. Differences significant only in lighting energy (26% lower) as lighting system operation profiles are also adapted to the presence and activity of occupants’. Despite different occupants’ behaviour within the day, difference of total annual primary energy demand between OP2 and OP1 for all comfort strategies varies between 4 and 5%. Occupants may have different preferences concerning the indoor temperatures during the occupied hours and may apply different management schemes for heating system (control strategies). Fig. 7 shows how alternative heating system management scenarios (see Table 4) are influencing energy consumption of the building at different occupation. It is seen that primary energy demand of the same building with different occupants and heating system strategies varies between 107 and 142 (kW h)/m2 . For example if we consider passive house, where limit for primary energy demand is 120 (kW h)/m2 , it is obvious that analysed building with occupancy profile OP1 and OP2 (4 occupants) will not reach this value at any heating strategy. The case COM1 was used as the base case for comparison with other control strategies. Results show (Fig. 7a) strategy COM3 (+20 ◦ C during the occupied hours) as the most energy efficient one. Using strategy COM3 with different occupancy profiles savings for heating increase by 21–36% compared to COM1, but this makes just 4–5% savings of total primary energy demand.
Fig. 6. Window of DesigBuilder hourly simulation results.
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Fig. 7. Influence of the occupancy profiles on annual energy demand ((kW h)/m2 ) of the building: (a) for heating; (b) electricity demand for lighting; (c) electricity demand for HVAC pumps and fans (auxiliary energy), (d) total primary energy.
4.2. Sensitivity analysis of results to energy efficiency level of the building and climate In order to estimate importance of occupancy information for different levels of energy efficiency of the building, some logical improvements for the best winter comfort strategy (COM3) were performed: lighting system was improved; to ensure summer comfort in the building, first shading together with improved ventilation and afterwards free cooling were added. Sensitivity analysis is performed for 3 alternative cities in similar climate as Kaunas.
4.2.1. Influence of lighting system improvements In well insulated and air tight houses, primary energy demand for heating constitutes just a small part of total primary energy demand. This is also confirmed by results presented in the paper. Therefore, next step in order to improve energy efficiency of the analysed building is lighting system improvement by adding stepped lighting control. The addition, lighting control enables to reduce energy for lighting by 28–34% (Fig. 7b) depending on the occupancy profile. Higher savings are observed for profiles OP2 (higher occupancy–more
spaces are used at the same time) and OP3 (more time spend at home). Influence on the heating and auxiliary energy exists, but it is insignificant. Total primary energy demand at different occupancy and comfort strategies has decreased to 99–132 (kW h)/m2 .
4.2.2. Influence of the shading and ventilation strategy Summer comfort analysis is performed for 4 cases: the most energy efficient strategy COM3 in combination with different occupancy profiles. After simulation of the external automatically controlled blinds that operate just during non-heating season, summer comfort has significantly improved (see Fig. 8a) for all analysed cases. Interesting fact is that occupation profiles also noticeably influence indoor temperatures (through heat gains and ventilation system operation schedules). The best summer comfort is observed for profiles OP3 and OP4, where overheating occurs just 1% of time during the year (when maximum set temperature is 26 ◦ C). Meanwhile this number reaches 9% for the default profiles supplied by the software library. Together with the shading, recirculated air flow of the system was twice decreased and heat recovery was switched off during cooling season. The only shading applied would increase energy demand for lighting and heating, but together with
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Fig. 10. Influence of occupation profiles on primary energy demand for different locations ((kW h)/m2 ).
occupants, the main reason for that is relatively high share of DHW energy in total primary energy balance.
Fig. 8. Overheating hours during the year (set temperature is 26 ◦ C): (a) blinds applied; (b) blinds and free cooling applied.
4.2.3. Influence of climate To assess the sensitivity of results to climate data, the simulation for three alternative cities (Warsaw, Tallinn and Stockholm) was performed. According to ECOFYS classification [54], Warsaw is situated in climate zone 3 (Eastern European), the rest of the analysed cities–are situated in Northern European climate zone 5. These cities are situated in cool climate zones (3000 < HDD18 ◦ C ≤ 4000). Sensitivity analysis do not include simulation of zones with drastic climate differences, because, as it was earlier told in introduction, people who live in the different climate zones behave differently, i.e. the profiles of their daily activities are different and therefore require separate research. The results of sensitivity analysis show (Fig. 10) that despite the fact that buildings’ energy performance varies in different cities, influence of the occupancy profiles gives nearly the same variation in results: 20–23%. These results lead to the conclusion, that occupancy information has the same level of importance in all Northern and part of Eastern Europe. 5. Conclusions
Fig. 9. Influence of occupation profiles on primary energy demand depending on efficiency of the building ((kW h)/m2 ).
more efficient ventilation for different profiles it gave additional total primary energy savings from 6 8% (Fig. 9). Additional option of free cooling was simulated, as a results the summer comfort improved for all of the occupancy profiles (Fig. 8b) and overheating period of 14 h per year occurred just for OP1. Such overheating is considered as acceptable. Meanwhile energy demand of the building did not increase (Fig. 9). It can be seen from Fig. 9 how total primary energy demand of the building was decreased step by step and how influence of the occupancy information has influenced the results. To make comparison conclusive, energy inefficient (insulation of the building is twice lower compared to the analysed building) case was added. Results show that the more building is energy efficient, the higher is the influence of occupants on total primary energy demand. For different building energy efficiency scenarios (Fig. 9), DesignBuilder default profiles have influence on the results within the limits of 14–21% compared to the alternative ones. Meanwhile final energy demand of separate systems can differ much more. More energy is always required for the households with 4
During planning and design stages of energy efficient houses investor and designer deal with requirement to ensure very low energy consumption in operational stage of building life cycle. Any deviation appeared after building commissioning will result noncompliance with regulatory requirements and loses. Authors rise the assumption that final energy demand significantly depends on occupancy related information. This study presents the modelling and prediction of buildings’ total primary energy demand by an intelligent complex approach, the cornerstone of which is the behaviour of building occupants’ (presence and activities at certain zones of building and respective profiles of daily energy consumption). The simulation results of the different cases revealed that the use of different occupancy profiles correlates with the total energy performance of the building. The comparison of default DesignBuilder profiles and analysed occupancy profile with 4 occupants (OP2) show that the influence of latter on overall primary energy demand for different cases is never exceeding 5%. This result lead to the conclusion that default profiles are applicable for the energy performance assessment only of households consisting of 4 occupants. The influence of households with 2 occupants (OP3, OP4) on overall primary energy demand varies from 14% to 21% compared to default profiles. The greater variation of results received mainly because of the lower DHW energy consumption. It was defined, that the occupancy information correlates mostly with the level
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of energy efficiency of the building: the less energy efficient the building, the less the influence of the occupancy information. The research also revealed a slight difference of the results for different cities of cool climate zones. That leads to the conclusion that results of this study are applicable also for other countries of Northern and partially of Eastern Europe. Importance of occupancy information in energy simulation depends also on complexity of the energy performance assessment. For example, if DHW energy demand is not included in energy performance assessment (less complex view is applied), the influence of the occupancy profiles on total primary energy demand in all analysed cases is not exceeding 10%. The conclusion follows, that default profiles are sufficient to be applied in energy performance calculations if DHW energy is not taken into account. Besides the influence on the predicted energy demand, different profiles also have influence on indoor comfort, especially in summer. Avoiding overheating is easier for households with very few occupants, because of decrease of their passive and active influences. If no sun shading is used, simulation with default DB profiles gives much higher (∼1000 h) number of overheating hours compared to the rest of the profiles. This issue needs additional investigation to find out the reasons.
Acknowledgement This study was funded by the Research Council of Lithuania supported project in National Research Programme “Future Energy” (No. ATE-03/2012). References [1] S. Asadi, S.S. Amiri, M. Mottahedi, On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design, Energy Build. 85 (2014) 246–255, http://dx.doi.org/10.1016/ j.enbuild.2014.07.096. [2] H. Rothrock, Sustainable housing: emergy evaluation of an off-grid residence, Energy Build. 85 (2014) 287–292, http://dx.doi.org/10.1016/ j.enbuild.2014.08.002. [3] N. Wang, H. Adeli, Sustainable building design, J. Civ. Eng. Manag. 20 (1) (2013) 1–10, http://dx.doi.org/10.3846/13923730.2013.871330. [4] D. Kaczorek, H. Koczyk, Structural solutions impact on thermal energy efficiency of heating surface at an open space, J. Civ. Eng. Manag. 19 (6) (2013) 883–893, http://dx.doi.org/10.3846/13923730.2013.840333 ˙ ˙ T. Vilutiene, ˙ A quantitative evaluation of [5] V. Zˇ ekas, V. Martinaitis, G. Streckiene, theoretical renewable energy potential of the building site, J. Civ. Eng. Manag. 20 (6) (2014) 873–883, http://dx.doi.org/10.3846/13923730.2014.976589 [6] A. Schlueter, F. Thesseling, Building information model based energy/exergy performance assessment in early design stages, Autom. Constr. 18 (2009) 153–163. [7] C. Bleil de Souza, Contrasting paradigms of design thinking: the building thermal simulation tool user vs. the building designer, Autom. Constr. 22 (2012) 112–122, http://dx.doi.org/10.1016/j.autcon.2011.09.008. [8] TRNSYS, TRNSYS—Simulation Environment for the Transient Simulation of Thermal Systems Including Multi-zone Buildings, Homepage, 2014, Available from http://www.transsolar.com/ software/docs/trnsys/trnsys uebersicht en.htm [cited 2014]. [9] IES Virtual Environment, IES Virtual Environment, Homepage, 2014, Available from http://www.iesve.com/content/ [cited 2014]. [10] EnergyPlus, EnergyPlus Energy Simulation Software, Homepage, 2014, from Available http://www.eere.energy.gov/buildings/energyplus/ [cited 2014]. [11] Jan. L.M. Hensen, R. Lamberts (Eds.), Building Performance Simulation for Design and Operation, Spon Press, Oxfordshire, UK, 2011, ISBN: 13: 978-0-41547414-6(hbk), 13:978-0-203-89161-2(ebk). [12] V. Motuziene, T. Vilutiene, Modelling the effect of the domestic occupancy profiles on predicted energy demand of the energy efficient house, Procedia Eng. 57 (2013) 798–807, http://dx.doi.org/10.1016/j.autcon.2012. 01.001. [13] C. Bleil de Souza, Studies into the use of building thermal physics to inform design decision making, Autom. Constr. 30 (2013) 81–93, http://dx.doi.org/10.1016/j.autcon.2012.11.026. [14] S.F. Larsen, C. Filippin, A. Beascochea, G. Lesino, An experience on integrating monitoring and simulation tools in the design of energy-saving buildings, Energy Build. 40 (6) (2008) 987–997, http://dx.doi.org/10.1016/ j.enbuild.2007.08.004.
[15] C.E. Ochoa, I.G. Capeluto, Advice for early design stages of intelligent facades based on energy and visual comfort, Energy Build. 41 (5) (2009) 480–488, http://dx.doi.org/10.1016/j.enbuild.2008.11.015. [16] S. Petersen, S. Svendsen, Method and simulation program informed decisions in the early stages of building design, Energy Build. 42 (7) (2010) 1113–1119, http://dx.doi.org/10.1016/j.enbuild.2010.02.002. [17] H. Kim, A. Stumpf, W. Kim, Analysis of an energy efficient building design through data mining approach, Autom. Constr. 20 (2011) 37–43, http://dx.doi.org/10.1016/j.autcon.2010.07.006. [18] A. Korjenic, T. Bednar, Validation and evaluation of total energy use in office buildings: a case study, Autom. Constr. 23 (2012) 64–70, http://dx.doi.org/10.1016/j.autcon.2012.01.001. [19] E. Azar, C.C. Menassa, A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings, Energy Build. 55 (2012) 841–853, http://dx.doi.org/10.1016/j.enbuild.2012.10.002. [20] F. Oldewurtel, D. Sturzenegger, M. Morari, Importance of occupancy information for building climate control, Appl. Energy 101 (2013) 521–532, http://dx.doi.org/10.1016/j.apenergy.2012.06.014. [21] T. de Meestera, A.-F. Mariqueb, A. De Herdea, S. Reiterb, Impacts of occupant behaviours on residential heating consumption for detached houses in a temperate climate in the northern part of Europe, Energy Build. 57 (2013) 313–323, http://dx.doi.org/10.1016/j.enbuild.2012.11.005. [22] D. Saelens, W. Parys, R. Baetens, Energy and comfort performance of thermally activated building systems including occupant behavior, Build. Environ. 46 (2011) 835–848, http://dx.doi.org/10.1016/j.buildenv.2010.10.012. [23] A.C. Menezes, A. Cripps, D. Bouchlaghem, R. Buswell, Predicted vs. actual energy performance of non-domestic buildings: using post- occupancy evaluation data to reduce the performance gap, Appl. Energy 97 (2012) 355–364, http://dx.doi.org/10.1016/j.apenergy.2011.11.075. [24] B. Bordass, R. Cohen, M. Standeven, A. Leaman, Assessing building performance in use 3: energy performance of the Probe buildings, Build. Res. Inf. 29 (2001) 114–128, http://dx.doi.org/10.1080/09613210010008036. [25] M. Sunikka-Blank, R. Galvin, Introducing the rebound effect: the gap between performance and actual energy consumption, Buil. Res. Inf. 40 (3) (2012) 260–273, http://dx.doi.org/10.1080/09613218.2012.690952. [26] D. Majcen, L.C.M. Itard, H. Visscher, Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: discrepancies and policy implications, Energy Policy 54 (2013) 125–136, http://dx.doi.org/10.1016/ j.enpol.2012.11.008. [27] B. Lin, H. Liu, A study on the energy rebound effect of China’s residential building energy efficiency, Energy Build. 86 (2015) 608–618, http://dx.doi.org/10.1016/j.enbuild.2014.10.049. [28] A.-L. Lindén, A. Carlsson-Kanyama, B. Eriksson, Efficient and inefficient aspects of residential energy behaviour: what are the policy instruments for change? Energy Policy 34 (2006) 1918–1927, http://dx.doi.org/ 10.1016/j.enpol.2005.01.015. [29] A. Carlsson-Kanyama, A.-L. Lindén, Energy efficiency in residences—challenges for women and men in the North, Energy Policy 35 (2007) 2163–2172, http://dx.doi.org/10.1016/j.enpol.2006.06.018. [30] R.V. Andersen, J. Toftum, K.K. Andersen, B.W. Olesen, Survey of occupant behaviour and control of indoor environment in Danish dwellings, Energy Build. 41 (2009) 11–16, http://dx.doi.org/10.1016/j.enbuild.2008.07.004. [31] J. Pett, P. Guertler, User behaviour in energy efficient homes, in: Phase 2, Report, 2004. [32] S. Kelly, M. Shipworth, D. Shipworth, M. Gentry, A. Wright, M. Pollitt, D. Crawford-Brown, K. Lomas, Predicting the diversity of internal temperatures from the English residential sector using panel methods, Appl. Energy 102 (2012) 601–621, http://dx.doi.org/10.1016/j.apenergy.2012.08.015. [33] J. Torriti, Demand side management for the European supergrid: occupancy variances of European single-person households, Energy Policy 44 (2012) 199–206, http://dx.doi.org/10.1016/j.enpol.2012.01.039. [34] Hetus, Harmonised European Time Use Survey. [online database version 2.0]. Created 2005-2007 by Statistics Finland and Statistics Sweden, http://www. tus.scb.se. [35] M. Santamouris, S.M. Alevizos, L. Aslanoglou, D. Mantzios, P. Milonas, I. Sarelli, S. Karatasou, K. Cartalis, J.A. Paravantis, Freezing the poor—indoor environmental quality in low and very low income households during the winter period in Athens, Energy Build. 70 (2014) 61–70, http://dx.doi.org/10.1016/ j.enbuild.2013.11.074. [36] M. Santamouris, On the energy impact of urban heat island and global warming on buildings, Energy Build. 82 (2014) 100–113, http://dx.doi.org/10.1016/ j.enbuild.2014.07.022. [37] A. Mahdavi, F. Tahmasebi, Predicting people’s presence in buildings: an empirically based model performance analysis, Energy Build. 86 (2015) 349–355, http://dx.doi.org/10.1016/j.enbuild.2014.10.027. [38] T. De Meester, A.-F. Marique, A. De Herde, S. Reiter, Impacts of occupant behaviours on residential heating consumption for detached houses in a temperate climate of the northern part of Europe, Energy Build. 57 (2012) 313–323, http://dx.doi.org/10.1016/j.enbuild.2012.11.005. [39] O. Guerra Santin, L. Itard, H. Visscher, The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock, Energy Build. 41 (2009) 1223–1232, http://dx.doi.org/10.1016/ j.enbuild.2009.07.002. [40] M. Deurinck, D. Saelens, S. Roels, Assessment of the physical part of the temperature takeback for residential retrofits, Energy Build. 52 (2012) 112–121.
V. Martinaitis et al. / Energy and Buildings 101 (2015) 64–75 [41] Z. Yu, B.C.M. Fung, F. Haghighat, H. Yoshino, E. Morofsky, A systematic procedure to study the influence of occupant behavior on building energy consumption, Energy Build. 43 (2011) 1409–1417, http://dx.doi.org/ 10.1016/j.enbuild.2011.02.002. [42] ISO 13790:2008, Energy Performance of Buildings—Calculation of Energy Use for Space Heating and Cooling, ISO, 2008. [43] J.A. Clarke, Energy Simulation in Building Design, second ed., ButterworthHeinemann, Oxford, 2013. [44] V. Vakiloroaya, Q.P. Ha, B. Samali, Energy-efficient HVAC systems: simulationempirical modelling and gradient optimization, Autom. Constr. 31 (2013) 176–185, http://dx.doi.org/10.1016/j.autcon.2012.12.006. [45] M. Kapsalaki, V. Leal, M. Santamouris, A methodology for economic efficient design of Net Zero Energy Buildings, Energy Build. 55 (2012) 765–778, http:// dx.doi.org/10.1016/j.enbuild.2012.10.022 [46] C.P. Underwood, F.W.H. Yik, Modelling Methods for Energy in Buildings, Blackwell Publishing, Oxford, 2004. ˙ V. Martinaitis, The framework of an optimization model for [47] V. Lapinskiene, building envelope, Procedia Eng. 57 (2013) 670–677, http://dx.doi.org/10. 1016/j.proeng.2013.04.085
75
˙ V. Martinaitis, E. Keras, Evaluation of energy efficiency meas[48] R. Mikuˇcioniene, ures sustainability by decision tree method, Energy Build. 76 (2014) 64–71, http://dx.doi.org/10.1016/j.enbuild.2014.02.048. [49] IWEC, Weather Files (International Weather for Energy Calculations), ASHRAE, Atlanta, GA, 2009. [50] STR, STR 2.01.09:2012 Pastatu˛ energinis naudingumas. Energinio naudingumo sertifikavimas (The Energy Efficiency in Buildings. The Energy Certification of Buildings), 2012. [51] NorthPass, Principles of low-energy houses applicable in North European countries and their applicability throughout the EU, in: NorthPass project report, October 2010, http://www.buildup.eu/publicaions/28371 [52] STR, STR 2.02.09:2005 Vienbuˇciai ir dvibuˇciai gyvenamieji pastatai (One and Two Flat Dwellings), 2005. ˛ u˛ ir visuomeniniu˛ pastatu˛ patalpu˛ [53] HN, HN 42:2009 Gyvenamuj mikroklimatas (Indoor Climate of Residential and Public Buildings), 2009. [54] ECOFYS, Towards nearly zero-energy buildings—definition of common principles under the EPBD, in: Final Report, February 2013, http://ec.europa.eu/ energy/sites/ener/files/documents/nzeb full report.pdf.
Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Importance of occupancy information when simulating energy demand of energy efficient house: A case study Vytautas Martinaitis a , Edmundas Kazimieras Zavadskas b,∗ , Violeta Motuziene˙ a , Tatjana Vilutiene˙ b a
Department of Building Energetics, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Sauletekio ave. 11, LT-10223 Vilnius, Lithuania Department of Construction Technology and Management, Faculty of Environmental Engineering, Vilnius Gediminas Technical University, Sauletekio ave. 11, LT-10223 Vilnius, Lithuania b
a r t i c l e
i n f o
Article history: Received 19 December 2014 Received in revised form 17 April 2015 Accepted 18 April 2015 Available online 25 April 2015 Keywords: Occupancy profile 3D model Energy efficient house Automated building energy systems Overheating Comfort strategy Auxilary energy
a b s t r a c t During planning and design stages of energy efficient houses investor and designer deal with requirement to ensure very low energy consumption in operational stage of building life cycle. Any deviation appeared after building commissioning will result non-compliance with regulatory requirements. Currently, the energy simulations of the residential building do not provide the approach, which would help comprehensively evaluate differences of occupants’ characteristics (age, behaviour, etc.), neither in energy performance certification methodologies nor in energy simulation software. The objective of study is to perform complex analysis of the effect of domestic occupancy profiles on the energy performance of energy efficient house and assess applicability of default DesignBuilder occupancy profiles at local conditions. Results show that the influence of the profile with 4 occupants (OP2) on overall primary energy demand for different cases is never exceeding 5% compared to default DesignBuilder profiles. The influence of households with 2 occupants (OP3, OP4) on overall primary energy demand varies from 14% to 21% compared to default profiles. The simulation results revealed that the use of different occupancy profiles correlates with the total energy performance of the building. Incorporation of occupancy information in energy simulations will improve the accuracy of the energy performance assessment. © 2015 Elsevier B.V. All rights reserved.
1. Introduction The negative environmental impacts of rapidly growing world energy consumption as well as vision of energy efficient sustainable buildings was discussed in the last decade [1–4] pointing that a successful creation of sustainable infrastructure systems, energy efficient and environmentally-conscious designs requires a holistic, integrated, and multidisciplinary approach. Unfortunately, because of the large number and diversity of participants, construction work is characterised by a fragmented decision-making process, fear of venture, and conservatism, which result in innovation avoidance and missed opportunities. In the processes of building design, operation and maintenance, the technological, institutional and cultural cooperation of architects, engineers and constructors is very important [5]. Due to the rising awareness of climate change and rise of
∗ Corresponding author. Tel.: +370 69820779. E-mail addresses: [email protected] (V. Martinaitis), [email protected], [email protected] (E.K. Zavadskas), ˙ [email protected] (T. Vilutiene). ˙ [email protected] (V. Motuziene), http://dx.doi.org/10.1016/j.enbuild.2015.04.031 0378-7788/© 2015 Elsevier B.V. All rights reserved.
challenging requirements for energy performance of buildings, the energy efficiency of buildings has to be estimated at an early design stage. Solutions made at early design stage determine building’s environmental performance during its entire life cycle. In order to evaluate the dependencies of performance criteria of form, material and technical systems, building performance assessment has to be integrated into the design process [6]. Computational analysis enables more precise and fast evaluation of building design alternatives, therefore researchers encourage using simulation tools throughout the whole building design process [7]. Most popular expert tools use physical calculation models for the calculation such as TRNSYS [8], IES Virtual Environment [9] or EnergyPlus [10]. Physical calculation models make the precise calculation of detailed tasks as well as overall energy consumption possible. From zone loads, daylighting and solar to multizone airflow, highly precise calculations for every possible engineering task are available. However, interactions between different simulation tools are limited, thus designer could meet difficulties in conducting fast and comprehensive building performance assessment. Many debates appear on rethinking and reassessing of what issues could be potentially addressed to allow the building
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simulation tools to be used in more efficient way throughout the whole building design process. Although there are many studies on prediction of energy performance in buildings, the resolution of simulation input information regarding occupancy (i.e. people’s presence and behaviour in buildings) is still rather low [11]. The aim of the article is to perform complex analysis of the effect of typical domestic occupancy profiles on the energy performance of energy efficient house and assess applicability of default DesignBuilder (DB) occupancy profiles at local conditions. In the previous study published [12], domestic hot water energy and summer comfort was not considered, therefore this study is extended to the analysis of energy demand required to ensure appropriate summer comfort in the building with the use of solar shading and alternative ventilation strategy. The paper also presents the framework for integrated building design that involves the analysis of the energy-related impacts of all building components, including the building location, envelope, heating, ventilation and air conditioning (HVAC), domestic hot water (DHW), lighting, controls, and equipment as well as the impact of occupancy characteristics and the application of simulation tool EnergyPlus (DesignBuilder). 2. Literature review 2.1. Integrated building design An integrated or whole building design process involves studies of the energy-related impacts and interactions of all building components, including the building location, envelope (walls, windows, doors, and roof), heating, HVAC, DHW, lighting, controls, and equipment [13–17] as well as the impact of occupancy characteristics [18–22]. Table 1 presents the examples of recent studies on application of new techniques and integrated building design process. 2.2. Rebound effect Buildings usually do not perform as predicted, even when very sophisticated energy simulation methods are used [23–27]. One of the reasons of such discrepancy between theoretical models and real building performance is the influence of human behaviour and the preferences of occupants. People affect the performance of buildings, due to their presence (passive effects) and their actions (active effects) [11] (Fig. 1). Energy and thermal performance of buildings is affected by people’s presence not just as a source of heat and water vapour, but also due to their actions, such as, operation of appliances, manipulation of building control devices for heating, cooling, ventilation and lighting, DHW consumption. Expected energy conservation cannot be fully achieved because the occupants might demand for more comfortable lifestyles with more
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energy services in buildings as building energy efficiency improves [27]. Scientists call this effect a ‘performance gap’ or ‘rebound effect’. The study [27] revealed that the rebound effect in the rural residential buildings is much larger than that in the urban residential buildings, this leads to the conclusion that one-flat residential buildings require more attention for energy simulation on design stage. 2.3. Energy consumption patterns For the prediction of energy consumption in building the energy consumption pattern in exact building has to be known. For modelling of building occupants’ passive and active effects, specialists usually rely on default load schedules of occupancy given in libraries of simulation tools. Occupancy profiles, heat load profiles as well as electricity load profiles in office buildings can be correctly predicted, whereas the absence of people in building and necessity for required comfort is known [19]. However, the prediction of energy consumption patterns in residential buildings is more complicated, especially in single family houses, since the diverse behaviour of households depend on many factors, such as: price of energy, users awareness of energy issues, gender, age, employment, family size, socio-cultural belonging, etc. [28–32]. The data on HVAC systems, electricity and domestic hot water systems, as well as energy consumption profiles is needed. The assumption made on activities that people perform when they are at home and awake enable to estimate indirectly the electricity consumption in buildings [33]. The occupancy pattern also depends on seasonality. Torriti [33] has explored electricity demand peaks in residential buildings concerning the time of active occupancy of single-person households in 15 European countries. The assessment of national variance in occupancy levels was made at the aggregate European level using Hetus (Harmonised European Time Use Survey) [34] statistical data of people’s use of time in different European countries. Analysis revealed that the differences in time spent for different activities depend on culture and climate zone. Study of Santamouris et al. [35] revealed that even the serious economic situation in the country has influence on the energy consumption and environmental quality of households. Results show that during the economic crisis in Greece indoor temperatures during the winter season 2012–2013 were much below the accepted standards and the energy consumption for heating was found to be minimum and much below the country’s threshold while a high fraction of households was not using heating energy at all. The specific study of Santamouris [36] makes clear that urban warming has also a very significant impact on the global energy consumption of buildings. The effect of occupants’ behaviour on building energy consumption was examined in many studies [37–41]. Recent studies analyse different factors related to human behaviour, for example, family size, the control mode of the heating system and management of the heated area [38]. Although
Table 1 Sample publications on application of new techniques and integrated building design process. Reference
Main topic
Schlueter and Thesseling [6] Bleil de Souza [7] Bleil de Souza [13] Larsen et al. [14] Ochoa and Capeluto [15] Petersen and Svendsen [16] Kim et al. [17] Korjenic and Bednar [18] Azar and Menassa [19] Oldewurtel et al. [20] de Meestera et al. [21] Saelens et al. [22]
Building information model based energy/exergy performance assessment in early design stages Debate on building thermal simulation tools to be better used throughout the whole building design process Study on the use of building thermal physics to inform design decision making Integrating simulation tools in the design of energy-saving buildings Early design stages of intelligent facades Simulation program informed decisions in the early stages of building design Analysis of an energy efficient building design Influence of building occupancy patterns and activities on total energy performance of building The impact of occupancy parameters in energy simulation of office buildings Importance of occupancy information for building climate control Impacts of occupant behaviours on residential heating consumption Energy and comfort performance of building systems including occupant behaviour
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Fig. 1. Active and passive occupants’ effects on building performance.
in the last decade a number of studies on the occupants’ behaviour impact have been carried out there is still need for highly transparent research designs and clearly defined statistics to evaluate the predictive performance of an occupancy model [37]. Summarizing the analysis, it could be concluded that in different countries inhabitants behave differently. Predicting the building energy demand, this fact has to be taken into account already at the early design stage.
important. Simulation tools considered as efficient instrument for increasing the total energy performance of building systems. Numerical or analytical integrated simulation techniques are the base of simulation tools. Integrated simulation methods can be divided into two groups: analytical and numerical. Clarke [43] and Underwood and Yik [46] describe them in details. Despite the fact that both of these groups have their advantages and disadvantages, both are suitable for the assessment of building energy performance.
2.4. Methods for assessing energy consumption in buildings 3. Case study In common engineering practice simplified building energy demand calculation methods prevail. Usually construction elements and engineering systems are evaluated separately, and human behaviour is assessed only minimally. According to ISO standard “Energy performance of buildings—Calculation of energy use for space heating and cooling” [42], energy demand calculations can be carried out using three alternative calculation methods: semi-stationary, simplified hourly dynamic and detailed dynamic methods. The building should be seen as a complex, dynamic and nonlinear system where parameters change at different speeds, and they depend on the thermodynamic state [43]. Therefore, there are doubts if simplified methods used in the engineering practice are always capable to evaluate complex systems, and the use of computer simulation programs is required. Incorporation of computational methods in architecture makes building performance analysis possible at the early design stage. Viewing the building as a system and revealing complex dependencies makes possible new approaches. Successful application of different simulation tools to minimise efficiently the overall energy consumption of the described in [6,18,44,45]. The results clearly illustrate that in order to reach the energy goals with economic efficiency the use of simulation tools at early design stage is very
A study presented in this paper has been carried out in the frame of the project “Building and Renewable Energy Sustainability Model” (hereinafter—the project) funded by the Research Council of Lithuania is presented in this paper. One of the aims of the project is to investigate energy demand of low energy buildings. This objective corresponds to the European Union’s targets for 2020: deployment of nearly zero energy buildings. Architectural and engineering solutions of buildings must be adapted according to the climatic conditions. The goal of the project is to develop and test a model for the assessment of the energy, exergy (thermodynamic) and ecological (environmental impact during the whole life cycle) efficiency of building energy systems (heating, ventilation, cooling, hot water systems, lighting) using renewable energy. The study is based on the assumption that the highest energy efficiency potential is on end-user side. In most cases, building energy systems function independently of each other. Interactions between different energy systems are not taken into account; therefore to optimise the energy consumption an integrated analysis of building and different building energy systems is required. The simulation engine EnergyPlus [10] has been used to simulate building’s annual energy demand with step of 1 h in this
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Fig. 2. Conceptual framework of main energy efficient building planning and design stages.
Fig. 3. Iterations in simulation steps.
study. The DesignBuilder, which is an interface of simulation engine EnergyPlus, was used to create the 3D building model. Simulation relies on successive substitution iteration to reconcile supply and demand using the Gauss–Seidell philosophy of continuous updating [12]. Fig. 2 presents the conceptual framework of main energy efficient building planning and design stages proposed by authors. This paper presents the analysis performed on stage 3 (simulation of building energy systems) of energy efficient building planning and design process (see Fig. 2). The analysis performed of other stages presented in [5,47,48]. The analysis performed includes following steps with iterations (Fig. 3):
5. Shading options were selected and simulated taking into account two criteria: minimal influence on heating demand and maximum influence on summer comfort. Together with shading, ventilation was improved. 6. To improve summer comfort alternative ventilation with free cooling was applied. 7. Analysis of influence of occupancy profiles on primary energy demand was performed in all of the above-mentioned steps. 8. Sensitivity analysis for different cool climate locations was performed.
1. Creation of model of the energy efficient building using default residential occupancy and systems operation profiles, supplied by the software library. 2. Creation and simulation of 3 alternative typical occupancy, lighting, DHW and HVAC system management schedules. 3. Creation and simulation of 4 different heating system management scenarios for 4 occupancy profiles (16 cases). 4. Improvements of lighting system for all cases were performed and simulated. Most energy efficient winter comfort strategy was identified for further summer comfort analysis.
3.1.1. Building geometry and structural elements Object of the analysis is one-storey, quadrate shape house (see Fig. 4, 3D model view and plan view in DesignBuilder), total area is 81 m2 . Several assumptions were made in creating the model:
3.1. Building description
• house occupation is standard family consisting of 4 persons (parents and 2 children); • appropriate thermal and lighting comfort corresponding to the requirements of national norms has to be maintained during the occupation hours;
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Fig. 4. Plan (a) and south fac¸ade (b) of the analysed building 3D model in DesignBuilder.
• the building is compact (small surface-area-to-volume ratio); • plan and geometry of the house created as a cell for developing a multi-storey residential building; and • building is planned to be energy efficient.
Structural elements of the building envelope were selected according to the requirements fixed in the national standard STR 2.01.09:2012 [50] for the buildings having energy performance label A+. Economical availability of construction materials was also
V. Martinaitis et al. / Energy and Buildings 101 (2015) 64–75 Table 2 U-values of the building envelope. Element
U-value (W/(m2 K)
Exterior wall Floor on the ground Roof Interior partitions Glazing Window frames
0.11 0.15 0.11 1.49 0.77 0.90
Table 3 Internal required temperatures (according to HN 42:2009 [54]). Room
Standard required temperature (◦ C)
Kitchen; bedroom 1, 2; lounge (living room) Bathroom Hall (circulation area)
18–22 20–23 14–16
taken into account. Sizes of windows in differently oriented facades were selected taking into account the requirements of the national standard STR 2.02.09:2005 [52] and recommendations given in [51], i.e. size of windows is minimal required to satisfy daylighting needs of the occupants and to minimize risk of overheating in summer and significant heat loss in winter. Thermal properties of the envelope of analysed house presented in Table 2.
3.1.2. Weather data The typical year data files were taken from the IWEC [49]. Typical meteorological weather data used for simulation represents Kaunas city (Lithuania). Temperatures for heating 19.3 ◦ C and for cooling 27.5 ◦ C.
3.1.3. Base case lighting It was assumed that lighting system operates during occupancy hours, when people are active. Lighting levels correspond to the national regulations [53]: bathroom—75 lx, bedrooms—200 lx, living room and kitchen—300 lx, hall—100 lx. Assumed specific power of illuminating devices is 3 W/m2 /100 lx. For the base case no lighting control was assumed.
3.1.4. Base case comfort Internal temperatures are set according to requirements of the standard [54] (see Table 3). Assuming that high comfort level is desired during the occupation hours, in the base case the highest allowed temperature is maintained. Since in reality occupants tend to heat their homes just at time of presence [40], therefore in the simulated house comfort temperature is maintained just during occupation hours adding 1 h before occupants come back home. For the base case, 3 ◦ C heating set back temperature (temperature that is maintained when rooms are unoccupied) is applied (except hall, where temperature is very low). Such temperature reduction has insignificant influence on comfort and moisture problems and leads to savings.
3.1.5. Base case ventilation Building has mechanical ventilation system with heat recovery. Fresh air flows parameters correspond to the requirements of national standards. Fresh air is supplied only during occupation hours and additional 1 h before occupants come back home. During unoccupied hours system operates in recirculation mode. The total average fresh air change rate in the building is 0.6 h−1 (includes infiltration and mechanical ventilation). This air change rate will be kept constant in all analysed below occupancy schedules, since supplied air flows were selected according to the area of the building, but not number of occupants.
3.1.6. Base case and alternative occupancy profiles Building occupancy profiles (schedules) describe how building and its systems operate and used to determine the character of influence on total energy consumption of the building. Occupancy is described separately for working days, weekends and holidays, i.e. schedules vary during the year. For the base case default occupancy schedules existent in the simulation software were assumed. Since occupancy profiles of residential building proposed by default in EnergyPlus software package do not correspond to the life style of northern Europe and they do not reflect the most common representative household composition and activities, more realistic alternatives were simulated and compared (see Table 4). Daily occupancy patterns for cases OP2–OP4 were created according to [38] and Hetus [34] data as well as taking into account inhabits of the Lithuanian occupants. Of course, there might also be other possible characteristics of households, but this study analyses only the most typical ones. The alternative occupancy profiles for the circulation area were not analysed since no data except DesignBuilder was found for the occupancy of this zone. Maximum occupation (when occupancy fraction is equal to 1) was assumed for premises: bedroom no. 1 (in case of OP1, OP2—bedroom of children, OP3—office, OP4—spare bedroom), (2) bedroom no. 2–2, kitchen—2 or 4, living room—2 or 4, bathroom—1. Daily occupancy profiles are expressed as fraction from the maximum occupation (Fig. 5). Differences of occupants’ behaviour during the year were not taken into account.
3.1.7. Base case and alternative heating strategies Comfort, as a rule, is understood by occupants differently. Even if we know occupation profiles quite well, it is difficult to predict their comfort preferences before building starts its operation. Therefore influence of the thermostat management (heating strategy) on energy demand of the building was analysed in combination with different occupation profiles. Properly using a programmable thermostat is one of the easiest ways to save energy without sacrificing comfort and sticking heating system’s settings to the occupant’s schedule. Four different thermal comfort strategies in all occupation schedules were analysed. COM1 and COM2 are strategies when high internal temperature is maintained during the occupation hours, but with different setback temperatures, COM3 strategy assumes the average temperatures during the occupied hours with temperature setback, and COM4 strategy the same as COM3, but without temperature setback (Table 5).
Table 4 Description of alternative occupancy profiles. Occupancy profile
Household characteristics
OP1 OP2
Standard residential occupancy profile for different zones of the building, household consists of 4 persons Household consists of 4 persons (parents are actively working, 2 children stay at school or kindergarten during the working hours; children come back home together with parents) Household consists of 2 persons (actively working) Household consists of 2 persons (retired couple)
OP3 OP4
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Fig. 5. Analysed daily occupancy profiles of building model: (a) alternative OP1; (b) alternative OP2; (c) alternative OP3; (d) alternative OP4.
Table 5 Analysed occupation strategies. Occupation strategies
COM1 COM2 COM3 COM4
Assumed set/setback temperatures (◦ C) Kitchen Bedroom 1, 2 Lounge
Bathroom
22/19 22/18 20/18 20
23/20 23/19 21/19 21
3.1.8. Improved summer comfort strategies It is well known that in new tight and highly insulated buildings the risk of overheating is likely to increase. The above described building with base case HVAC and lighting systems also has faced overheating problem for all of the occupancy scenarios analysed. Summer design temperature is not met 1916–2823 h a year depending on the occupancy profile and temperatures are peaking at 36 ◦ C. Most convenient response to a problem of overheating is to invest in a local active cooling system, but it will undo many of the reductions in energy consumption and carbon dioxide emissions achieved by the improvements made to building. Furthermore, it is certainly not a sustainable solution considering the climate in Lithuania, where the risk of overheating can be addressed effectively by passive design measures.
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In order to solve overheating problem, two measures were analysed: solar shading and alternative ventilation strategy (decreased recirculated air flow) with free cooling option (using economiser). An economiser is a damper opening that draws up to 100% outside air when the outside air is cooler than the temperature inside the building, thereby providing free cooling. Recirculated air flow in the constant volume mechanical ventilation system was also decreased twice to minimise energy demand of fans. Different internal and external (static and movable) shading alternatives were considered for the building. Shading makes positive effect on summer comfort of the house, but it also more or less makes negative effect on the heating energy demand. Therefore final decision choosing shading was made considering two criteria: minimum effect on heating energy demand and maximum effect on overheating hours. External blinds, operating just during non-heating season and operated according to the inside air temperature, were selected.
4. Simulation results 4.1. Influence of occupancy profiles, heating strategies and lighting control Four different occupancy profiles were simulated for base case comfort strategy during the period of one year using the time step of 1 h (Fig. 6). Results of simulation (Fig. 7) show that, if different occupancy profiles are considered for the same building, significant differences in energy consumption may be expected. Vivid differences appear in occupational strategies were household composed with less than four persons. The results for occupancy profile OP3 (only two adult occupants) for the base case show the increase of 17% for heating and 6% for electricity consumed for lighting, and the reduction of 2% for auxiliary equipment compared with the case OP2 (see Fig. 7a–c). Moreover, in the case with retired couple (OP4), results show a 30% increase in energy consumption for heating, 4% increase in electricity consumption for auxiliary equipment and 16% rise
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in energy consumption for lighting compared with the case OP2. These increases related to longer operation of building systems. Looking at the total primary energy consumption (Fig. 7d) (primary energy factor applied for electricity—2.8) of the building, it is obvious that house with occupancy profile OP1 (default DB profile) is the least energy efficient and OP3—the most efficient one. Since DHW is an important energy using system of residential building, number of occupants makes a significant influence on the overall energy performance of the building. Therefore, taking into account DHW primary energy demand causes obvious differences in performance of the building with two and four occupants. At the same time results for realistic occupancy profiles of Eastern–Northern European family (OP2) show relatively small differences comparing with the default profile (OP1) available in the simulation tool DesignBuilder. Differences significant only in lighting energy (26% lower) as lighting system operation profiles are also adapted to the presence and activity of occupants’. Despite different occupants’ behaviour within the day, difference of total annual primary energy demand between OP2 and OP1 for all comfort strategies varies between 4 and 5%. Occupants may have different preferences concerning the indoor temperatures during the occupied hours and may apply different management schemes for heating system (control strategies). Fig. 7 shows how alternative heating system management scenarios (see Table 4) are influencing energy consumption of the building at different occupation. It is seen that primary energy demand of the same building with different occupants and heating system strategies varies between 107 and 142 (kW h)/m2 . For example if we consider passive house, where limit for primary energy demand is 120 (kW h)/m2 , it is obvious that analysed building with occupancy profile OP1 and OP2 (4 occupants) will not reach this value at any heating strategy. The case COM1 was used as the base case for comparison with other control strategies. Results show (Fig. 7a) strategy COM3 (+20 ◦ C during the occupied hours) as the most energy efficient one. Using strategy COM3 with different occupancy profiles savings for heating increase by 21–36% compared to COM1, but this makes just 4–5% savings of total primary energy demand.
Fig. 6. Window of DesigBuilder hourly simulation results.
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Fig. 7. Influence of the occupancy profiles on annual energy demand ((kW h)/m2 ) of the building: (a) for heating; (b) electricity demand for lighting; (c) electricity demand for HVAC pumps and fans (auxiliary energy), (d) total primary energy.
4.2. Sensitivity analysis of results to energy efficiency level of the building and climate In order to estimate importance of occupancy information for different levels of energy efficiency of the building, some logical improvements for the best winter comfort strategy (COM3) were performed: lighting system was improved; to ensure summer comfort in the building, first shading together with improved ventilation and afterwards free cooling were added. Sensitivity analysis is performed for 3 alternative cities in similar climate as Kaunas.
4.2.1. Influence of lighting system improvements In well insulated and air tight houses, primary energy demand for heating constitutes just a small part of total primary energy demand. This is also confirmed by results presented in the paper. Therefore, next step in order to improve energy efficiency of the analysed building is lighting system improvement by adding stepped lighting control. The addition, lighting control enables to reduce energy for lighting by 28–34% (Fig. 7b) depending on the occupancy profile. Higher savings are observed for profiles OP2 (higher occupancy–more
spaces are used at the same time) and OP3 (more time spend at home). Influence on the heating and auxiliary energy exists, but it is insignificant. Total primary energy demand at different occupancy and comfort strategies has decreased to 99–132 (kW h)/m2 .
4.2.2. Influence of the shading and ventilation strategy Summer comfort analysis is performed for 4 cases: the most energy efficient strategy COM3 in combination with different occupancy profiles. After simulation of the external automatically controlled blinds that operate just during non-heating season, summer comfort has significantly improved (see Fig. 8a) for all analysed cases. Interesting fact is that occupation profiles also noticeably influence indoor temperatures (through heat gains and ventilation system operation schedules). The best summer comfort is observed for profiles OP3 and OP4, where overheating occurs just 1% of time during the year (when maximum set temperature is 26 ◦ C). Meanwhile this number reaches 9% for the default profiles supplied by the software library. Together with the shading, recirculated air flow of the system was twice decreased and heat recovery was switched off during cooling season. The only shading applied would increase energy demand for lighting and heating, but together with
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Fig. 10. Influence of occupation profiles on primary energy demand for different locations ((kW h)/m2 ).
occupants, the main reason for that is relatively high share of DHW energy in total primary energy balance.
Fig. 8. Overheating hours during the year (set temperature is 26 ◦ C): (a) blinds applied; (b) blinds and free cooling applied.
4.2.3. Influence of climate To assess the sensitivity of results to climate data, the simulation for three alternative cities (Warsaw, Tallinn and Stockholm) was performed. According to ECOFYS classification [54], Warsaw is situated in climate zone 3 (Eastern European), the rest of the analysed cities–are situated in Northern European climate zone 5. These cities are situated in cool climate zones (3000 < HDD18 ◦ C ≤ 4000). Sensitivity analysis do not include simulation of zones with drastic climate differences, because, as it was earlier told in introduction, people who live in the different climate zones behave differently, i.e. the profiles of their daily activities are different and therefore require separate research. The results of sensitivity analysis show (Fig. 10) that despite the fact that buildings’ energy performance varies in different cities, influence of the occupancy profiles gives nearly the same variation in results: 20–23%. These results lead to the conclusion, that occupancy information has the same level of importance in all Northern and part of Eastern Europe. 5. Conclusions
Fig. 9. Influence of occupation profiles on primary energy demand depending on efficiency of the building ((kW h)/m2 ).
more efficient ventilation for different profiles it gave additional total primary energy savings from 6 8% (Fig. 9). Additional option of free cooling was simulated, as a results the summer comfort improved for all of the occupancy profiles (Fig. 8b) and overheating period of 14 h per year occurred just for OP1. Such overheating is considered as acceptable. Meanwhile energy demand of the building did not increase (Fig. 9). It can be seen from Fig. 9 how total primary energy demand of the building was decreased step by step and how influence of the occupancy information has influenced the results. To make comparison conclusive, energy inefficient (insulation of the building is twice lower compared to the analysed building) case was added. Results show that the more building is energy efficient, the higher is the influence of occupants on total primary energy demand. For different building energy efficiency scenarios (Fig. 9), DesignBuilder default profiles have influence on the results within the limits of 14–21% compared to the alternative ones. Meanwhile final energy demand of separate systems can differ much more. More energy is always required for the households with 4
During planning and design stages of energy efficient houses investor and designer deal with requirement to ensure very low energy consumption in operational stage of building life cycle. Any deviation appeared after building commissioning will result noncompliance with regulatory requirements and loses. Authors rise the assumption that final energy demand significantly depends on occupancy related information. This study presents the modelling and prediction of buildings’ total primary energy demand by an intelligent complex approach, the cornerstone of which is the behaviour of building occupants’ (presence and activities at certain zones of building and respective profiles of daily energy consumption). The simulation results of the different cases revealed that the use of different occupancy profiles correlates with the total energy performance of the building. The comparison of default DesignBuilder profiles and analysed occupancy profile with 4 occupants (OP2) show that the influence of latter on overall primary energy demand for different cases is never exceeding 5%. This result lead to the conclusion that default profiles are applicable for the energy performance assessment only of households consisting of 4 occupants. The influence of households with 2 occupants (OP3, OP4) on overall primary energy demand varies from 14% to 21% compared to default profiles. The greater variation of results received mainly because of the lower DHW energy consumption. It was defined, that the occupancy information correlates mostly with the level
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of energy efficiency of the building: the less energy efficient the building, the less the influence of the occupancy information. The research also revealed a slight difference of the results for different cities of cool climate zones. That leads to the conclusion that results of this study are applicable also for other countries of Northern and partially of Eastern Europe. Importance of occupancy information in energy simulation depends also on complexity of the energy performance assessment. For example, if DHW energy demand is not included in energy performance assessment (less complex view is applied), the influence of the occupancy profiles on total primary energy demand in all analysed cases is not exceeding 10%. The conclusion follows, that default profiles are sufficient to be applied in energy performance calculations if DHW energy is not taken into account. Besides the influence on the predicted energy demand, different profiles also have influence on indoor comfort, especially in summer. Avoiding overheating is easier for households with very few occupants, because of decrease of their passive and active influences. If no sun shading is used, simulation with default DB profiles gives much higher (∼1000 h) number of overheating hours compared to the rest of the profiles. This issue needs additional investigation to find out the reasons.
Acknowledgement This study was funded by the Research Council of Lithuania supported project in National Research Programme “Future Energy” (No. ATE-03/2012). References [1] S. Asadi, S.S. Amiri, M. Mottahedi, On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design, Energy Build. 85 (2014) 246–255, http://dx.doi.org/10.1016/ j.enbuild.2014.07.096. [2] H. Rothrock, Sustainable housing: emergy evaluation of an off-grid residence, Energy Build. 85 (2014) 287–292, http://dx.doi.org/10.1016/ j.enbuild.2014.08.002. [3] N. Wang, H. Adeli, Sustainable building design, J. Civ. Eng. Manag. 20 (1) (2013) 1–10, http://dx.doi.org/10.3846/13923730.2013.871330. [4] D. Kaczorek, H. Koczyk, Structural solutions impact on thermal energy efficiency of heating surface at an open space, J. Civ. Eng. Manag. 19 (6) (2013) 883–893, http://dx.doi.org/10.3846/13923730.2013.840333 ˙ ˙ T. Vilutiene, ˙ A quantitative evaluation of [5] V. Zˇ ekas, V. Martinaitis, G. Streckiene, theoretical renewable energy potential of the building site, J. Civ. Eng. Manag. 20 (6) (2014) 873–883, http://dx.doi.org/10.3846/13923730.2014.976589 [6] A. Schlueter, F. Thesseling, Building information model based energy/exergy performance assessment in early design stages, Autom. Constr. 18 (2009) 153–163. [7] C. Bleil de Souza, Contrasting paradigms of design thinking: the building thermal simulation tool user vs. the building designer, Autom. Constr. 22 (2012) 112–122, http://dx.doi.org/10.1016/j.autcon.2011.09.008. [8] TRNSYS, TRNSYS—Simulation Environment for the Transient Simulation of Thermal Systems Including Multi-zone Buildings, Homepage, 2014, Available from http://www.transsolar.com/ software/docs/trnsys/trnsys uebersicht en.htm [cited 2014]. [9] IES Virtual Environment, IES Virtual Environment, Homepage, 2014, Available from http://www.iesve.com/content/ [cited 2014]. [10] EnergyPlus, EnergyPlus Energy Simulation Software, Homepage, 2014, from Available http://www.eere.energy.gov/buildings/energyplus/ [cited 2014]. [11] Jan. L.M. Hensen, R. Lamberts (Eds.), Building Performance Simulation for Design and Operation, Spon Press, Oxfordshire, UK, 2011, ISBN: 13: 978-0-41547414-6(hbk), 13:978-0-203-89161-2(ebk). [12] V. Motuziene, T. Vilutiene, Modelling the effect of the domestic occupancy profiles on predicted energy demand of the energy efficient house, Procedia Eng. 57 (2013) 798–807, http://dx.doi.org/10.1016/j.autcon.2012. 01.001. [13] C. Bleil de Souza, Studies into the use of building thermal physics to inform design decision making, Autom. Constr. 30 (2013) 81–93, http://dx.doi.org/10.1016/j.autcon.2012.11.026. [14] S.F. Larsen, C. Filippin, A. Beascochea, G. Lesino, An experience on integrating monitoring and simulation tools in the design of energy-saving buildings, Energy Build. 40 (6) (2008) 987–997, http://dx.doi.org/10.1016/ j.enbuild.2007.08.004.
[15] C.E. Ochoa, I.G. Capeluto, Advice for early design stages of intelligent facades based on energy and visual comfort, Energy Build. 41 (5) (2009) 480–488, http://dx.doi.org/10.1016/j.enbuild.2008.11.015. [16] S. Petersen, S. Svendsen, Method and simulation program informed decisions in the early stages of building design, Energy Build. 42 (7) (2010) 1113–1119, http://dx.doi.org/10.1016/j.enbuild.2010.02.002. [17] H. Kim, A. Stumpf, W. Kim, Analysis of an energy efficient building design through data mining approach, Autom. Constr. 20 (2011) 37–43, http://dx.doi.org/10.1016/j.autcon.2010.07.006. [18] A. Korjenic, T. Bednar, Validation and evaluation of total energy use in office buildings: a case study, Autom. Constr. 23 (2012) 64–70, http://dx.doi.org/10.1016/j.autcon.2012.01.001. [19] E. Azar, C.C. Menassa, A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings, Energy Build. 55 (2012) 841–853, http://dx.doi.org/10.1016/j.enbuild.2012.10.002. [20] F. Oldewurtel, D. Sturzenegger, M. Morari, Importance of occupancy information for building climate control, Appl. Energy 101 (2013) 521–532, http://dx.doi.org/10.1016/j.apenergy.2012.06.014. [21] T. de Meestera, A.-F. Mariqueb, A. De Herdea, S. Reiterb, Impacts of occupant behaviours on residential heating consumption for detached houses in a temperate climate in the northern part of Europe, Energy Build. 57 (2013) 313–323, http://dx.doi.org/10.1016/j.enbuild.2012.11.005. [22] D. Saelens, W. Parys, R. Baetens, Energy and comfort performance of thermally activated building systems including occupant behavior, Build. Environ. 46 (2011) 835–848, http://dx.doi.org/10.1016/j.buildenv.2010.10.012. [23] A.C. Menezes, A. Cripps, D. Bouchlaghem, R. Buswell, Predicted vs. actual energy performance of non-domestic buildings: using post- occupancy evaluation data to reduce the performance gap, Appl. Energy 97 (2012) 355–364, http://dx.doi.org/10.1016/j.apenergy.2011.11.075. [24] B. Bordass, R. Cohen, M. Standeven, A. Leaman, Assessing building performance in use 3: energy performance of the Probe buildings, Build. Res. Inf. 29 (2001) 114–128, http://dx.doi.org/10.1080/09613210010008036. [25] M. Sunikka-Blank, R. Galvin, Introducing the rebound effect: the gap between performance and actual energy consumption, Buil. Res. Inf. 40 (3) (2012) 260–273, http://dx.doi.org/10.1080/09613218.2012.690952. [26] D. Majcen, L.C.M. Itard, H. Visscher, Theoretical vs. actual energy consumption of labelled dwellings in the Netherlands: discrepancies and policy implications, Energy Policy 54 (2013) 125–136, http://dx.doi.org/10.1016/ j.enpol.2012.11.008. [27] B. Lin, H. Liu, A study on the energy rebound effect of China’s residential building energy efficiency, Energy Build. 86 (2015) 608–618, http://dx.doi.org/10.1016/j.enbuild.2014.10.049. [28] A.-L. Lindén, A. Carlsson-Kanyama, B. Eriksson, Efficient and inefficient aspects of residential energy behaviour: what are the policy instruments for change? Energy Policy 34 (2006) 1918–1927, http://dx.doi.org/ 10.1016/j.enpol.2005.01.015. [29] A. Carlsson-Kanyama, A.-L. Lindén, Energy efficiency in residences—challenges for women and men in the North, Energy Policy 35 (2007) 2163–2172, http://dx.doi.org/10.1016/j.enpol.2006.06.018. [30] R.V. Andersen, J. Toftum, K.K. Andersen, B.W. Olesen, Survey of occupant behaviour and control of indoor environment in Danish dwellings, Energy Build. 41 (2009) 11–16, http://dx.doi.org/10.1016/j.enbuild.2008.07.004. [31] J. Pett, P. Guertler, User behaviour in energy efficient homes, in: Phase 2, Report, 2004. [32] S. Kelly, M. Shipworth, D. Shipworth, M. Gentry, A. Wright, M. Pollitt, D. Crawford-Brown, K. Lomas, Predicting the diversity of internal temperatures from the English residential sector using panel methods, Appl. Energy 102 (2012) 601–621, http://dx.doi.org/10.1016/j.apenergy.2012.08.015. [33] J. Torriti, Demand side management for the European supergrid: occupancy variances of European single-person households, Energy Policy 44 (2012) 199–206, http://dx.doi.org/10.1016/j.enpol.2012.01.039. [34] Hetus, Harmonised European Time Use Survey. [online database version 2.0]. Created 2005-2007 by Statistics Finland and Statistics Sweden, http://www. tus.scb.se. [35] M. Santamouris, S.M. Alevizos, L. Aslanoglou, D. Mantzios, P. Milonas, I. Sarelli, S. Karatasou, K. Cartalis, J.A. Paravantis, Freezing the poor—indoor environmental quality in low and very low income households during the winter period in Athens, Energy Build. 70 (2014) 61–70, http://dx.doi.org/10.1016/ j.enbuild.2013.11.074. [36] M. Santamouris, On the energy impact of urban heat island and global warming on buildings, Energy Build. 82 (2014) 100–113, http://dx.doi.org/10.1016/ j.enbuild.2014.07.022. [37] A. Mahdavi, F. Tahmasebi, Predicting people’s presence in buildings: an empirically based model performance analysis, Energy Build. 86 (2015) 349–355, http://dx.doi.org/10.1016/j.enbuild.2014.10.027. [38] T. De Meester, A.-F. Marique, A. De Herde, S. Reiter, Impacts of occupant behaviours on residential heating consumption for detached houses in a temperate climate of the northern part of Europe, Energy Build. 57 (2012) 313–323, http://dx.doi.org/10.1016/j.enbuild.2012.11.005. [39] O. Guerra Santin, L. Itard, H. Visscher, The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock, Energy Build. 41 (2009) 1223–1232, http://dx.doi.org/10.1016/ j.enbuild.2009.07.002. [40] M. Deurinck, D. Saelens, S. Roels, Assessment of the physical part of the temperature takeback for residential retrofits, Energy Build. 52 (2012) 112–121.
V. Martinaitis et al. / Energy and Buildings 101 (2015) 64–75 [41] Z. Yu, B.C.M. Fung, F. Haghighat, H. Yoshino, E. Morofsky, A systematic procedure to study the influence of occupant behavior on building energy consumption, Energy Build. 43 (2011) 1409–1417, http://dx.doi.org/ 10.1016/j.enbuild.2011.02.002. [42] ISO 13790:2008, Energy Performance of Buildings—Calculation of Energy Use for Space Heating and Cooling, ISO, 2008. [43] J.A. Clarke, Energy Simulation in Building Design, second ed., ButterworthHeinemann, Oxford, 2013. [44] V. Vakiloroaya, Q.P. Ha, B. Samali, Energy-efficient HVAC systems: simulationempirical modelling and gradient optimization, Autom. Constr. 31 (2013) 176–185, http://dx.doi.org/10.1016/j.autcon.2012.12.006. [45] M. Kapsalaki, V. Leal, M. Santamouris, A methodology for economic efficient design of Net Zero Energy Buildings, Energy Build. 55 (2012) 765–778, http:// dx.doi.org/10.1016/j.enbuild.2012.10.022 [46] C.P. Underwood, F.W.H. Yik, Modelling Methods for Energy in Buildings, Blackwell Publishing, Oxford, 2004. ˙ V. Martinaitis, The framework of an optimization model for [47] V. Lapinskiene, building envelope, Procedia Eng. 57 (2013) 670–677, http://dx.doi.org/10. 1016/j.proeng.2013.04.085
75
˙ V. Martinaitis, E. Keras, Evaluation of energy efficiency meas[48] R. Mikuˇcioniene, ures sustainability by decision tree method, Energy Build. 76 (2014) 64–71, http://dx.doi.org/10.1016/j.enbuild.2014.02.048. [49] IWEC, Weather Files (International Weather for Energy Calculations), ASHRAE, Atlanta, GA, 2009. [50] STR, STR 2.01.09:2012 Pastatu˛ energinis naudingumas. Energinio naudingumo sertifikavimas (The Energy Efficiency in Buildings. The Energy Certification of Buildings), 2012. [51] NorthPass, Principles of low-energy houses applicable in North European countries and their applicability throughout the EU, in: NorthPass project report, October 2010, http://www.buildup.eu/publicaions/28371 [52] STR, STR 2.02.09:2005 Vienbuˇciai ir dvibuˇciai gyvenamieji pastatai (One and Two Flat Dwellings), 2005. ˛ u˛ ir visuomeniniu˛ pastatu˛ patalpu˛ [53] HN, HN 42:2009 Gyvenamuj mikroklimatas (Indoor Climate of Residential and Public Buildings), 2009. [54] ECOFYS, Towards nearly zero-energy buildings—definition of common principles under the EPBD, in: Final Report, February 2013, http://ec.europa.eu/ energy/sites/ener/files/documents/nzeb full report.pdf.