Top PDF Occupant behavior and energy consumption in dwellings:

Occupant behavior and energy consumption in dwellings:

Occupant behavior and energy consumption in dwellings:

This thesis has been interested in determining occupant behavior 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 occupant behavior, this research revealed that household composition, age, income, ownership of dwelling, and education are the most important elements of behavioral profiling. 
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Occupant behavior and energy consumption in dwellings: An analysis of behavioral models and actual energy consumption in the dutch housing stock

Occupant behavior and energy consumption in dwellings: An analysis of behavioral models and actual energy consumption in the dutch housing stock

This thesis has been interested in determining occupant behavior 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 occupant behavior, this research revealed that household composition, age, income, ownership of dwelling, and education are the most important elements of behavioral profiling. 
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Bottom-up statistical analysis of the energy consumption of French single-family dwellings

Bottom-up statistical analysis of the energy consumption of French single-family dwellings

We conducted a covariance analysis (ANCOVA, generalisation of a multiple linear regression with both quantitative and qualitative explanatory variables), based on the ordinary least squares estimator. The natural logarithm of the total energy consumption of 420 single-family dwellings was taken as response variable (dependent variable). Since we were seeking the main determinants of the latter amongst a very large number of explanatory variables (38 variables), we started by selecting variables according to their contribution to the model. For this purpose, we recursively removed the variable that made the smallest contribution, by analyzing the probability associated with the F-test of Type III SS (null hypothesis test: "The variance of the model with the variable is not significantly different from that of the model without the variable (null variable coefficient(s))."), until only variables with a probability of no more than 0.15 remained. The table of the Type III SS (Sum of Squares) shows how—regardless of the order of selection of the variables in the model—removing one explanatory variable affects the adjustment of the model, all other variables being kept. Since this first selection was conducted only according to the global contribution of the variables, we applied the method of backward selection to the model as second selection.
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Effects of occupant behavior on the energy performance of dwellings

Effects of occupant behavior on the energy performance of dwellings

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 occupant behavior has increased recently, as newly designed dwellings have not achieved expected energy performance levels, leading to the possibility that occupant behavior is a factor in their underperformance (Guerra Santín and Itard,  2010). Although expected occupant behavior 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 occupant behavior on building energy performance is essential to meet  the growing demand for more sustainable buildings (Hoes, et al., 2008).
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Assessing the effect of new control and payment methods on heating energy consumption and occupant behaviour in Chinese dwellings

Assessing the effect of new control and payment methods on heating energy consumption and occupant behaviour in Chinese dwellings

65 thermal sensation. From the survey it was found that the measurement of PMV model can be use in new dwellings to predict the occupants of actual thermal sensations (AMV) according to ISO 7730. Furthermore, occupants in old dwellings provide more satisfied and thermal neutral than that in new air-conditioned buildings. Dick and Thomas (1951) reported that 70% of the observed variance of open vents and casements could be accounted for by the outdoor air temperature, based on field measurements carried out in 15 houses during 26 winter weeks. Additionally, they suggested that another 10% of the observed variance was contributed by the wind speed. In their study, the wind speed and direction, the inside and outside air temperatures were measured and recorded automatically using particular devices, and the state of windows was recorded manually. Moreover, previous researchers report about thermal comfort on winter conditions related to energy consumption in residential buildings. Seligman et al conducted survey in 500 homes at Twin Rivers in the eastern USA and they observed that homeowners’ summer electricity consumption could be predicted by comfort and health concerns (Seligman, et al., 1977/78). It also found that the greater the importance of personal comfort and ‘health’ to the household, the higher the consumption for air-conditioning.
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Adaptive comfort control implemented model (ACCIM) for energy consumption predictions in dwellings under current and future climate conditions: A case study located in Spain

Adaptive comfort control implemented model (ACCIM) for energy consumption predictions in dwellings under current and future climate conditions: A case study located in Spain

Abstract: Currently, the knowledge of energy consumption in buildings of new and existing dwellings is essential to control and propose energy conservation measures. Most of the predictions of energy consumption in buildings are based on fixed values related to the internal thermal ambient and pre-established operation hypotheses, which do not reflect the dynamic use of buildings and users’ requirements. Spain is a clear example of such a situation. This study suggests the use of an adaptive thermal comfort model as a predictive method of energy consumption in the internal thermal ambient, as well as several operation hypotheses, and both conditions are combined in a simulation model: the Adaptive Comfort Control Implemented Model (ACCIM). The behavior of ACCIM is studied in a representative case of the residential building stock, which is located in three climate zones with different characteristics (warm, cold, and mild climates). The analyses were conducted both in current and future scenarios with the aim of knowing the advantages and limitations in each climate zone. The results show that the average consumption of the current, 2050, and 2080 scenarios decreased between 23% and 46% in warm climates, between 19% and 25% in mild climates, and between 10% and 29% in cold climates by using such a predictive method. It is also shown that this method is more resilient to climate change than the current standard. This research can be a starting point to understand users’ climate adaptation to predict energy consumption.
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A visual energy performance assessment and decision support tool for dwellings

A visual energy performance assessment and decision support tool for dwellings

The building physics based models consider detailed information about the building and hence estimate energy consumption with most clarity (Larsen and Nesbakken 2004). Furthermore, they do not depend upon historical values; however, the historical data can be used to calibrate the models. The major advantage of building physics based models are the modular structure of their algorithms. This means the users of this approach can easily modify the algorithms to suit particular needs (Kavgic et al. 2010). Building physics based models are the only methods that can fully esti- mate energy consumption of a sector without historical energy consumption information and evaluate the im- pact of new technologies (Swan and Ugursal 2009). The policies and initiatives such as CERT, CESP, ECO and Green Deal require practical decisions and are directed towards the level of the physical factors which influ- ence energy use. Bottom-up approaches and in par- ticular the building physics based models help in addressing these needs and hence is the preferred ap- proach in this study.
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Split Incentives and Energy Efficiency in Canadian Multi-Family Dwellings

Split Incentives and Energy Efficiency in Canadian Multi-Family Dwellings

keep their apartments warmer while they are out than those who pay for their own heat. This effect is at least partially mitigated by the landlord’s provision of more energy- efficiency technologies in these apartments. Evidence of a usage effect is also provided in Munley et al (1990) who, using data from the late 1970s, find that in otherwise identical blocks of centrally heated apartments (equipped with identical appliances) where one half of tenants paid their own electricity bills, those tenants who had their electricity costs included in their rent used on average a little over 30% more electricity than their counterparts. Further evidence of efficiency problems is found by Davis (2009) who, using a subset of observations from the US 2005 Residential Energy Consumption Survey that excludes dwellings with utility–included rental payments, finds that owner-occupied dwellings are more likely than rental dwellings to have at least one Energy Star product in each appliance category. Finally, a set of case studies commissioned by the International Energy Agency estimate the proportion of energy use that is subject to split-incentives or other barriers for a variety of sectors
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An EnergyPlus whole building energy model calibration method for office buildings using occupant behavior data mining and empirical data

An EnergyPlus whole building energy model calibration method for office buildings using occupant behavior data mining and empirical data

The first step of the calibration is to replace the TMY3 weather file (DOE 2013) with real weather information in accordance with the actual data collection period. The second step is to replace the design case occupancy schedules with the “real (or learned)” occupancy schedules generated from the data mining study. The third step is to calibrate the interior lighting, interior equipment, exterior lighting, and exterior equipment power densities and schedules with monthly and hourly energy consumption data with an “inversed calibration method”. The fourth and final step is to calibrate HVAC system parameters and controls. It is important that the HVAC system should be calibrated after other input parameters and systems are calibrated, because most of these inputs will influence the HVAC system performance (such as internal loads and “disturbances”). The calibration acceptance criterion for each calibration step are MBE<5% and CVRMSE<15% for monthly data calibration, and MBE<10% and CVRMSE<30% for hourly data calibration, respectively. MBE and CVRMSE are defined by Equation (1 - 3).
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The thermal energy performance of domestic dwellings in the UK

The thermal energy performance of domestic dwellings in the UK

The measurement of energy consumption in domestic properties, as discussed by the interviewees, could be viewed as a mixed method case study (Johnson and Onwuegbuzie, 2004) reflecting the range of qualitative and quantitative factors as identified below. While larger statistical studies can describe performance at the highest level, such as Shipworth et al. (2010), the housing energy fact file (Palmer et al., 2011) or the national energy efficiency database framework (DECC, 2011), the BPE professionals interviewed here look at the underlying reasons that shape energy performance and so have a more detailed focus that considers the interrelationships between elements of individual properties or groups of properties. Boundary conditions have a major impact on the performance of the properties (Karlsson and Moshfegh, 2006). Clearly, the external temperature will influence internal temperature and so must be measured. In addition, energy inputs from solar gain in the fabric and through glazing will impact the internal temperature of the property (CEBE, 2010). Wind will impact the performance of the fabric as it alters the convective heat loss of elements and can also lead to wind washing (Ito et al., 1972; Yazdanian and Klems, 1994), and although not widely researched, rain has an impact on the conductivity performance of the building fabric (Blocken and Carmeliet, 2004).
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Thermal comfort and energy  related occupancy behavior in Dutch residential dwellings

Thermal comfort and energy related occupancy behavior in Dutch residential dwellings

the dwellings of this study would lead to considering some of them as being out of the comfortable zone in March, while occupants reported feeling ‘neutral’. Although our  sample, by its small size and its characteristics, cannot claim to be representative for all dwellings in the Netherlands, it has been possible, by using the Fisher’s test, to indicate which actions can be considered habitual or do relate to thermal sensation. Extending  the study to more dwellings, our measurement method, by which the reported thermal sensation is measured many times a day and coupled to physical data, will allow the collection of more accurate data on actual comfort. Furthermore, the MRT and air velocities were not measured in situ. This was compensated by building simulations with Energy+ [31], but these parameters should be measured in further studies. De Dear [18,22] mentions that the adaptive model does not really provide any insight  into why certain conditions will be comfortable or acceptable, other than a broad generalization that they conform to occupants’ expectations. The indoor temperatures  would lead the adaptive model to assume that the tenants were comfortable, having already performed the adaptive actions aimed to create thermal comfort and a ‘neutral’  thermal sensation. Yet, this was not the case, and the tenants’ non-neutral feelings  might lead them to perform additional acts, which could come at the expense of energy  consumption, especially because the tenants in the monitoring study reported that economic factors played no role in their energy consumption.
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Thermal comfort and energy related occupancy behavior in Dutch residential dwellings

Thermal comfort and energy related occupancy behavior in Dutch residential dwellings

the dwellings of this study would lead to considering some of them as being out of the comfortable zone in March, while occupants reported feeling ‘neutral’. Although our  sample, by its small size and its characteristics, cannot claim to be representative for all dwellings in the Netherlands, it has been possible, by using the Fisher’s test, to indicate which actions can be considered habitual or do relate to thermal sensation. Extending  the study to more dwellings, our measurement method, by which the reported thermal sensation is measured many times a day and coupled to physical data, will allow the collection of more accurate data on actual comfort. Furthermore, the MRT and air velocities were not measured in situ. This was compensated by building simulations with Energy+ [31], but these parameters should be measured in further studies. De Dear [18,22] mentions that the adaptive model does not really provide any insight  into why certain conditions will be comfortable or acceptable, other than a broad generalization that they conform to occupants’ expectations. The indoor temperatures  would lead the adaptive model to assume that the tenants were comfortable, having already performed the adaptive actions aimed to create thermal comfort and a ‘neutral’  thermal sensation. Yet, this was not the case, and the tenants’ non-neutral feelings  might lead them to perform additional acts, which could come at the expense of energy  consumption, especially because the tenants in the monitoring study reported that economic factors played no role in their energy consumption.
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Structural Equation Modeling of Effective Economic and Cultural Components on Energy Consumption Behavior in Urban Societies

Structural Equation Modeling of Effective Economic and Cultural Components on Energy Consumption Behavior in Urban Societies

percent more than the global average. As well as other issues such as the fact that the energy consumption of any household in Iran is over 2900 kW / h, which is more than three times compared to the global average annual consumption of electricity per household (900 kWh). The per capita consumption of gasoline in Iran is six times of the global average, and gas per capita is more than triple and water per capita is twice the global average (Khoshkhooy, 2015). Given the considerations such as the sanctions conditions of the Iranian economy, the strong dependence of the Iranian economy on energy resources, particularly the decreasing resources of oil and gas, increasing energy consumption in Iran, the continued reduction of energy reserves and the production of them, the inefficient and profitable pattern of energy consumption in Iran, and the energy efficiency index has declined in the past decade, which is one of the targeted components of resistance policy, it is imperative that comprehensive, efficient and coherent policies to be designed and implemented to improve efficiency and productivity of energy consumption in Iran because consumption in a community plays an important role in determining the type, amount and form of production and distribution, and on the other hand, it is influenced by the conditions and culture of society. If the situation of society is such that it leads people to use more, most of the resources of society are consumed, the level of savings will be reduced and there will be no suitable ground for investment. It also brings down production and social poverty, and economic weakness provides the basis for cultural poverty and social degradation and increases the vulnerability of society to crises (Vahida et.al., 2010).
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3.4. Energy Consumption in Cities. The urban metabolism Energy consumption is the largest contributor to

3.4. Energy Consumption in Cities. The urban metabolism Energy consumption is the largest contributor to

The total ecological footprint can be subdivided into specific categories of consumption and waste production. The carbon footprint is calculated as the area of forest that would be required to absorb CO 2 emissions from fossil fuel combustion, excluding the proportion absorbed by the oceans. The biomass fuel footprint is calculated as the area of forest needed to grow wood and other forest products used as fuel. The hydropower footprint is the area occupied by hydroelectric dams and reservoirs. The energy footprint – of both carbon-based and nuclear-generated energy sources – accounts for more than half of the total world ecological footprint. At the city scale, the energy footprint is even more dominant.
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Structural Equation Modeling of Effective Economic and Cultural Components on Energy Consumption Behavior in Urban Societies

Structural Equation Modeling of Effective Economic and Cultural Components on Energy Consumption Behavior in Urban Societies

Abstract: Pre-purchase is a way to buy housing with consumption-investment motivation in uncertainty condition with high risk and direct financial investment. This research aims to identify and prioritize effective factors on willingness to pre-purchase demand of housing in the city of Ilam and to present a conceptual model. In terms of purpose, this research is applied and it is descriptive-analytical with mixed approach (qualitative and quantitative) in terms of method. To collect data, library and field studies were used. Data collection tools are interview and researcher-made questionnaire. The validity was confirmed by experts and confirmative factor analysis. The reliability was calculated by Cronbach’s alpha. In qualitative stage, population including 40 experts (from road and urbanization, housing foundation, Maskan Bank, and real estate experts) of Ilam selected by purposive sampling method. In the quantitative stage, population included buyers of under construction houses determined 240 ones based on Morgan table and selected by available sample sampling method. To analyze data, SPSS 16 and Lisrel 8.5 were used. The results indicated that economic, financial, fiscal-behavioral, motivational, and social factors influence on housing pre-purchase and economic factors including poverty, economic efficiency and economic crisis, with the coefficient of 4.57, ranked first and political ones- economic policies- ranked the last with the coefficient of 2.66.
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Extended abstracts of final reports in the field of solar energy applications to dwellings. EUR 7977 EN. Energy

Extended abstracts of final reports in the field of solar energy applications to dwellings. EUR 7977 EN. Energy

COMPUTER MODELLING OF SOLAR HEATING SYSTEMS USING AN ADSORBENT AS HEAT STORAGE MEDIUM A FIRST MODEL WAS SET UP TO DESCRIBE THE BEHAVIOUR OF A STORAGE SYSTEM EXPOSED TO A STEP CHANGE OF T[r]

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Energy waste in buildings due to occupant behaviour

Energy waste in buildings due to occupant behaviour

Over the past 20 years, many studies have been carried out to better understand occupants’ energy behaviour in buildings [13,14], with the aim of producing more energy efficient buildings through changing occupants’ of the systems within the building [15]. In order to help building occupants decide how to change their behaviour to have a more energy efficient behavior, tailored advice was often required [16,17]. To help prepare this ‘tailored’ advice, building engineers have used building simulation to quantify the impact of changing occupant behaviour on the performance of the building [18]. However, due to the lack of data with respect to occupants’ actual behaviour in buildings, building engineers have had to use assumed extreme behavioural patterns to drive the simulation, e.g. heating always on [19] or windows/doors always open [19,20], resulting in a lack of confidence in the predicted impact.
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Occupant-oriented energy control by taking the human in the control loop of building systems

Occupant-oriented energy control by taking the human in the control loop of building systems

Parys divided the research towards user behavioural into six fields, shown in Figure 2. Occupancy can be considered as one of the research fields in user behaviour. Because, being present within the building is clearly a necessary condition to interact with it. The other research fields are the control of solar shading (B), window deployment (C), control of the lighting (D), the use of electrical appliances (E) and the control of the thermal environment (F) by the occupant. This list is not exhaustive as it is restricted to actions that change the environment and thus influence the building’s energy demands [7]. Other adaptive actions like adjusting clothing, having a drink or changing the activity level, better known as personal or intermediate actions, are not included. Interactions with the buildings’ environmental systems are difficult or even impossible to predict at the level of an individual person [6]. The building occupant performs these control actions to improve his personal comfort level. Removing these possibilities from the building occupant to influence his environment is not an option, because the ability to self-regulate his environment are critical factors for satisfaction of the occupant [8].
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ABSTRACT :Energy monitoring and consumption device implements a low power energy consumption,

ABSTRACT :Energy monitoring and consumption device implements a low power energy consumption,

(AMR). The AMR are classified in two types, Wireless and Wired. The energy meters with closeness, bluetooth energy meters were designed which communicated wirelessly with master PC which were basically for low power consumption. This technique wasn’t efficient as it operated within a short range. The wired system, the Power Line Carrier (PLC) and the Telephone Line Network are the AMR systems available. To transmit the data every month remotely to the central office, tele watt meters were used. This was done with the help of dedicated telephone lines and a pair of modems. A command was send to the remote meters by the master PC at the control centre to get the data using Power Line Communication technique, which was mainly implemented in the areas with fixed telephone net
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Consumption & Savings Behavior in Pakistan

Consumption & Savings Behavior in Pakistan

It can be seen that all variables are statistically significant at 5% level of significance. Model is overall significant as well with adjusted R 2 = 0.8630. The signs of the coefficients are also in line with theory and expectation. Real GDP growth rate and exports to GDP ratio have a positive impact on national savings rate. This shows that both consumption and savings are normal goods and positively associated with income.

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