This review first helped to set up a reference point for the reasons to actual occupantbehavior, how perception, lifestyle, norms, rules lead to various actions at home (Figure 1). Secondly, through this study, a framework for the relationship between occupantbehavior and energy consumption was created (Figure 2), based on the determinants of behavior, i.e. occupant characteristics (educational, economic, social), dwelling characteristics (envelope, systems, lighting and appliances…). This literature study set the context and also the first steps of this research. The determinants found through this review (Table 2) gave input to the content and structure of the questions of the survey designed for the OTB dataset.
Andersen (2009) made a theoretical study on a single room with a single occupant in Copenhagen, focusing on different comfort levels (3 PMV factors) and behavioral modes (naïve and rational) and their impact on primary energy consumption. The occupantbehavior in the study referred to the use of table fan, window opening, blinds, and heating, in reaction to the perception of comfort. In this respect naïve behavior means to turn on the table fan at 0,03 PMV, to open the window at 0,06 PMV, to drawn the blinds at 0,09 PMV, to remove clothing garment at 0,11 PMV, and finally the to turn off the heating beyond 0,17 PMV. Rational behavior, on the other hand, is assumed as more considerate reaction to the perception of comfort, such as turning off the heat in the first step, rather than turning on the table fan. The result is that the naïve behavior results in 3 times more energy use than the rational (3948 kWh/year-1198kWh/year). Tanimoto et al’s (2008) research on single dwellings in Tokyo proposed a method to predict the peak energy requirement for cooling, that combines an algorithm that generates short-term events that are likely to occur in residences, and the stochastic variations in these short-term events. Research about simulating behavior either by statistics or by simulation programs, deal with office spaces, on a single zone model, or more zones with less details on use, more articulation on movement. This underlines the gap in the research field of modelling occupantbehavior in residences, in a manner that involves both use of space and circulation patterns, and in relation to the dwelling energy performance.
This thesis has been interested in determining occupantbehavior in relation to energy consumption, claiming that the buildings’ energy consumption can be validated in total, only during occupancy, when the design is tested on actual use. Referring to the lack of research, this study combined the deductive (cross-sectional, macro data, macro level statistics) and the inductive methods (longitudinal data, detailed high frequency data, performance simulation), by considering both the determinants of behavior and the actual behavior itself. We found that deductive methods are much faster in calculating and dissecting energy consumption into its factors, such as household characteristics, dwelling characteristics, behavioral aspects, etc; and inductive methods model actual behavior from bottom up experimenting and validating energy consumption levels. In addition, this research has found that the heating energy consumption of a dwelling is the most sensitive to thermostat control, followed respectively by ventilation control and presence. Both heating energy consumption and indoor resultant temperature are the most robust to radiator control. Calculating a regression model on the determinants of electricity consumption, this research has found that using the total duration of appliance use and parameters of household size, dwelling type, number of showers, use of dryer and washing cycles, and presence in rooms, 58% of the variance in electricity consumption could be explained. Introducing behavioral profiles and patterns contribute to the modeling of energy consumption and occupantbehavior, this research revealed that household composition, age, income, ownership of dwelling, and education are the most important elements of behavioral profiling.
Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Because of the stochastic nature of occupantbehavior, the number of people occupying a space and the duration occupied is a non-trivial aspect to characterize. Literature studies have focused on the impact of occupancy presence scenarios on energy use in office buildings, with Gunay et al.  providing a comprehensive and up-to-date critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices.
Despite the primary intention aimed at improving building energy efficiency [ 17 ], studies revealed that some green-certified building consume more energy compared to conventional buildings. For instance, energy comparison between US-based certification, Leadership in En- ergy and Environmental Design (LEED) [ 18 ], and conventional build- ings showed that, on average, energy performance of LEED-certified buildings is better, however, many individual LEED buildings consume more energy than their conventional counterparts [ 19 , 20 ]. This energy gap may, to a large extent, be attributed to occupants’ behavior in LEED buildings [ 21 , 22 ], which is otherwise known to play an important role in building energy consumption [ 23 , 24 ]. Vice versa, characteristics of buildings also influence the behavior of occupants [ 25 ]. For instance, window opening behavior is strongly dependent on a dwelling type, ventilation strategy, and heating system [ 26 ]. However, the effect of building energy certification on occupantbehavior remains ambiguous: some research reported that occupants in green buildings adopt more pro-environmental behavior than those in conventional buildings [ 27–29 ], while others showed that the building certification does not motivate such behavior [ 30 , 31 ]. Influence of energy certification on occupant comfort, satisfaction and self-reported health symptoms is relatively understudied. Some survey-based studies argue that green-certified buildings provide more satisfying environments and fewer health symptoms compared to conventional buildings [ 32 , 33 ]. In contrary, other researchers found negligible differences in occupants’ satisfactions between green-certified and conventional buildings [ 34 , 35 ]. A recent survey in four Minergie office buildings in Switzerland showed that users’ satisfaction with air temperature and indoor air quality was lower than 50% [ 36 ].
This paper proposes a method comprising procedures to calibrate an EnergyPlus whole building energy model. An occupantbehavior data mining procedure is developed and tested in an office building. Workday occupancy schedules are generated by mining the office appliance energy consumption data. Hourly and monthly power, energy, and temperature data are collected and used for lighting, equipment and HVAC systems energy performance calibration. The result shows a 1.27% mean bias error for the total annual energy use intensity. The proposed calibration method provides a scientific and systematic framework to conduct high accuracy EnergyPlus model calibration.
The amount of energy consumed by a building depends on the characteristics of the building’s envelope; the service systems installed for heating and ventilation, electricity, and hot water; the site and climate in which the building is located; and the behavior of its occupants. Occupants interact with a dwelling in order to achieve the indoor comfort conditions they require or to engage in certain activities. These interactions can include regulating the indoor temperature; opening windows or grilles; switching lights on or off; or intermediate actions involving the operation of lighting and devices, such as watching TV, reading, studying, eating, and performing household activities. Research on occupantbehavior has increased recently, as newly designed dwellings have not achieved expected energy performance levels, leading to the possibility that occupantbehavior is a factor in their underperformance (Guerra Santín and Itard, 2010). Although expected occupantbehavior is taken into consideration during the design process for concept buildings, designers do not know exactly how a building and its user(s) will interact before the building is occupied. A more accurate understanding of the effects of occupantbehavior on building energy performance is essential to meet the growing demand for more sustainable buildings (Hoes, et al., 2008).
Regarding the financial benefits resulting from a shift of energy, for scenario 1 savings of 35.1 e /a have been achieved for a three person household. It is apparent from Table IV that laundry and dish washing contribute most to the benefits of a variable tariff structure. While the model used here assumes an occupant starts the appliance, wet appliances can generally be equipped with a timer or some form of automated controller, which will increase the viability of DSM. In contrast, cooking, PC-use, watching TV as well as cleaning and ironing seem to offer little potential for shifting, which may be due to the fact that those appliances are commonly not used at hours when a high price was applied, or that the user response and thus the willingness to adapt is considerably lower for those activities (see Figure 6). About 10% of the electricity consumption for ironing and cleaning was shifted, which indicates a general potential for flexibility of those appliances, but since their share of the total electricity consumption is only 3%, the impact is almost negligible.
Regarding the financial benefits resulting from a shift of energy, for scenario 1 savings of 35.1 e/a have been achieved for a three person household. It is apparent from Table IV that laundry and dish washing contribute most to the benefits of a variable tariff structure. While the model used here assumes an occupant starts the appliance, wet appliances can generally be equipped with a timer or some form of automated controller, which will increase the viability of DSM. In contrast, cooking, PC-use, watching TV as well as cleaning and ironing seem to offer little potential for shifting, which may be due to the fact that those appliances are commonly not used at hours when a high price was applied, or that the user response and thus the willingness to adapt is considerably lower for those activities (see Figure 6). About 10% of the electricity consumption for ironing and cleaning was shifted, which indicates a general potential for flexibility of those appliances, but since their share of the total electricity consumption is only 3%, the impact is almost negligible.
An analysis of the hypothesis that wider stairwells correspond to faster overall evacuation times is most reliable when all other variables are held constant. In WTC 1 on September 11, 2001, however, many other variables were not constant, complicating the comparison: (a) Stairwell B (56 in. wide) exited into the Concourse, while Stairwells A and C (44 in.) exited to the Mezzanine where occupants typically traversed to the escalator in order to descend to the Concourse; (b) Stairwell B only required one (relatively short) horizontal transfer section, while Stairwells A and C required multiple, sometimes lengthy (over 100 ft) horizontal transfers; (c) emergency response personnel preferentially used Stairwell B to climb to higher floors; and (d) an occupant may have switched stairwells during egress, introducing a significant uncertainty. Therefore, these four factors confound any conclusions regarding stairwell width which may be drawn from the evacuation of the WTC towers. Respondents reported three pieces of information critical to this analysis: number of floors they had to climb down, the total time spent in the stairwells, and which stairwell they used. Each reported time was normalized (divided by) with the number of floors descended in order to compare results independently of starting location. The normalized times were then averaged over all occupants who reported using that stairwell. In Stairwell B in WTC 1, the average occupant spent approximately 61 ± 38 s per floor, while in Stairwells A and C (the narrower stairwells), the average occupant spent 53 ± 34 s per floor. The
The introduction of local control system in existing building, like thermostatic radiator valves acting on each room, allows not only to obtain better comfort conditions but also a general reduction in heating energy consumptions. Anyway this control system combined with local energy metering systems, brings the user to a more conscious behavior, sometimes leading it to maintain indoor thermal condition lower than usual with the purpose of spending less. Also different ways of occupancy can lead to variable internal set point during day or week.
Table 4 lists some basic information of occupantbehavior differences for all monitored offices. The percentage of window-opening state has been used to distinguish the differences in occupantbehavior. From Table 4, it can be seen that the window-opening rates for all five offices were between 15.0% and 48.7%, and Rooms 202, 206 and 208 had more time with open windows.On the other hand, the average times for each occupant to open their window in a week has been used to judge the frequency of occupantbehavior. The difference in average time for each occupant can also reflect the contribution of personal preference. It was defined a low frequency as the times from 0 to 2.4, an average frequency when the times from 2.4 to 5.6, and a high frequency when the time more than 5.6. The result in the last column of Table 4 illustrates that Room 205, 206 have the most frequent occupantbehavior.
Tuohy, P.G. and Humphreys, M.A. and Nicol, F. and Rijal, H.B. and Clarke, J.A. (2009) Occupant behaviour in naturally ventilated and hybrid buildings. American Society of Heating Refrigerating and Air Conditioning Engineers (ASHRAE) Transactions, 115 (1). pp. 16-27. ISSN 0001-2505 Strathprints is designed to allow users to access the research output of the University of Strathclyde. Copyright c
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Also, evolving design of modern offices and working environments must continue to expect high levels of spatial and technological change through the provision of suitable Indoor Environmental Quality (IEQ) in support of intensive computer laden or paper work . While the aforementioned has implicitly shaped changes in building design and the control of the indoor working environment, the need to determine occupant perception and comfort remains overriding with a keen focus on its implication on energy use. Post Occupancy Evaluation is one such tool at achieving this objective. POE is described in general terms as a broad range of activities aimed at understanding how buildings perform once they are built . In other literature, it is also defined as a process in which a building has to be evaluated in a systematic and accurate manner after it has been built and occupied for some time , . Extant studies such as , have also defined it as any and all activities that originate out of an interest in learning how a building performs once it is built, including if and how well it has met expectations . Essentially, the value of systematic learning from POE is primarily in twofold. One identifies additional benefits that can be obtained per the evaluation of the building; allowing for minor tuning to enhance its performance for the users . Such cases, involve a typical scope to achieve enhancements at a nil or low cost. And this, can involve measures such as, incorporating energy efficient elements into the fit-out or a change in the building management including user protocols. The second benefit, takes the form of a guidance on follow up procurement . Thus, this ensures that successful aspects that the users endorse are incorporated in future projects along with the aspects of the building that warrant improvement to occupant comfort.
All interviews and video tours were recorded and fully transcribed. Empirical data was initially coded manually (Braun & Clarke, 2006), using two coding manuals for the data (Schreier, 2012): one for the interviews with PV professionals and POs and one for the occupant interviews and video tours . Other codes emerged freely during the analysis process, aiding the identification of specific themes (Pallasmaa, 2009). The coding was verified using an inter-coder method to increase the reliability (Krippendorff, 2004). A mapping method (Yaneva, 2012) was subsequently used to analyse the coding in relation to the critical actants in terms of whether they acted as mediators - making the decisions, or as intermediaries - impacting on the decisions. The provision data was also mapped to compare the PV governance structure in each case study, and between the two contrasting sets of housing projects, and the findings are discussed next in terms of Professionals and Occupants governance.
Bourgois, Reinhart and Macdonald (2006) have created the facility in their sub-hourly occupancy model (SHOCC) to manage occupancy patterns, behavioural algorithms and associated heat gains or losses across the multiple zones of a building within a dynamic simulation. They illustrated this facility using the algorithms of Lightswitch-2002 and predicted the effect of occupant and automatic control of blind and lights on lighting energy use. The SHOCC module has been implemented in conjunction with ESP-r and its framework is extendable and could in future include the thermal and olfactory behaviours mentioned above.
Paul Du Bois, Clifford C. Chou, Bahig B. Fileta, Tawfik B. Khalil,, Albert I. King, Harold F. Mahmood and JacWismans, “Vehicle Crashworthiness and Occupant Protection”, Edited by Priya Prasad and Jamel E. Belwafa, American Iron and Steel Institute, Michigan, 2004.
Mercantile and Business Occupancies voted to change the occupant load from the current 100 square feet per person to 150 square feet per person, based upon technical substantiation that was provided. At their ROC, the TC voted to change the occupant load back to 100 square feet per person based upon several comments that were received and the committee's need to more documentation and justification. The committee was in agreement that this is an issue they must address in the future, but was not able to determine the correct action during this cycle due to the lack of technical support for the issue.