Occupants have little influence on the overall energy consumption in district heated apartment buildings

The purpose of this research is twofold: first, to evaluate how occupant behavior in a neutral environment influences the overall greenhouse gas emissions and energy consumption of multi-family apartment buildings, and second: to establish which activities associated with housing companies produce the most greenhouse gas emissions. The research comprised of a multiple cases of housing companies dating from the late 1960s and located in the Finnish capital Helsinki. The studied housing companies represent the least energy efficient third of the Finnish residential building stock, approaching obligatory large scale refurbishments. The analysis is conducted in two phases with a so called hybrid life-cycle assessment (LCA) technique. Using the technique, both economic and metric consumption data are analyzed to estimate the greenhouse gas emissions generated by activities associated with housing. In line with previous research, the study identifies heating energy as the single most significant contributor to greenhouse gas emissions. The results also show that the carbon load associated with housing activities makes up one third of an individual resident's overall carbon footprint. Contrary to often stated belief, the study indicates that occupant behavior has only limited effect on the energy consumption and, consequently, carbon emissions derived from housing, particularly when multi-family housing connected to district heating is concerned. However, apartment size seems to have a two-way impact, the smallest and the largest being the least energy efficient.     

Artificial intelligence and networking in integrated building management systems

In recent years the emphasis has moved towards integrating all a building's systems via centralised building management systems (BMS). To provide a more intelligent approach to the facility management, safety and energy control in building management systems (IBMS), this paper proposes a methodology for integrating the data within a BMS via a single multi-media networking technology and providing the BMS with artificial intelligence (AI) through the use of knowledge-based systems (KBS) technology. By means of artificial intelligence, the system is capable of assessing, diagnosing and suggesting the best solution. This paper outlines how AI techniques can enhance the control of HVAC systems for occupant comfort and efficient running costs based on occupancy prediction. Also load control and load balancing are investigated. Instead of just using pre-programmed load priorities, this work has investigated the use of a dynamic system of priorities which are based on many factors such as area usage, occupancy, time of day and real time environmental conditions. This control strategy which is based on a set of rules running on the central control system, makes use of information gathered from outstations throughout the building and communicated via the building's data-bus.     

Effect of thermostat and window opening occupant behavior models on energy use in homes

Existing dynamic energy simulation tools exceed the static dimension of the simplified methods through a better and more accurate prediction of energy use; however, their ability to predict real energy consumption is undermined by a weak representation of human interactions with the control of the indoor environment. The traditional approach to building dynamic simulation considers energy consumption as fully deterministic, taking into account standardized input parameters and using fixed and unrealistic schedules (lighting level, occupancy, ventilation rate, thermostat set-point). In contrast, in everyday practice occupants interact with the building plant system and building envelope in order to achieve desired indoor environmental conditions. In this study, occupant behavior in residential building was modelled accordingly to a probabilistic approach. A new methodology was developed to combine probabilistic user profiles for both window opening and thermostat set-point adjustments into one building energy model implemented in the dynamic simulation tool IDA Ice. The aim of the study was to compare mean values of the probabilistic distribution of the obtained results with a singular heating energy consumption value obtained by means of standard deterministic simulations. Major findings of this research demonstrated the weakness of standardized occupant behavior profile in energy simulation tools and the strengths of energy models based on measurements in fields and probabilistic modelling providing scenarios of occupant behavior in buildings.     

Behavioral variables and occupancy patterns in the design and modeling of Nearly Zero Energy Buildings

The objective of obtaining high performance energy buildings can be reached considering the contemporaneous effects of technical characteristics and occupancy. Recent studies report that as buildings become more energy efficient, the behavior of occupants plays an increasing role in consumption. Therefore, a construction designed to be a Nearly Zero Energy Building (nZEB) might generate higher consumption than expected if the assumptions made in the simulation process are not respected during the real use. The occupant can modify the control strategies of internal variables (heating/cooling system operation, set point temperature, ventilation, lighting) and the users’ behavior has a high impact on the utilization of plants and equipment. A significant contribution is also represented by the internal gains that have a direct relation with occupancy. The aim of this study is to assess the influence of housing occupancy patterns on the definition of residential nZEB in Italian climatic conditions. The investigation has been carried out considering a case study consisting of a building designed according to the National Standards. Successively, different conditions of the building usage are analyzed using dynamic energy simulations that allow exploration of the different occupation modes. The variability of the family composition and the occupancy scenarios are defined based on the data collected in the specific context. The investigation provides information regarding the effects of human variables (occupants’ needs and preferences) on the final energy performance of low energy buildings and highlights the combination of variables that are important in the definition of nZEB as net zero source energy.     

Case studies of energy consumption in residential buildings in Russia's middle belt area

This paper describes the analytical results of heating energy consumption monitoring and the determination of heat protection levels for both single-family and multi-family buildings in Russia. Experiments have been jointly conducted by Russian and American researchers from the Research Institute for Building Physics, Gosstroy of Russia, and the Natural Resources Defense Council. The paper describes qualimetric methods which provide a quantitative estimation of thermal and energy performance by using instrumental facilities and non-destructive methods.

The first Section gives a brief methodological summary for determining on-site building energy and thermal performance. The second part reviews the field results of energy monitoring to determine techniques that may result in the highest energy conservation effect for standard single-family houses in Russia. The third Section presents a comparative energy analysis of the use of highly insulating windows in a multi-family apartment building in field conditions. The experiments conducted prove that it is possible to ensure a 20–25% reduction in energy consumption for residential buildings under actual conditions existing in Russia. Finally, a methodology for assessing whole-building energy consumption for multi-family buildings is described.     

Occupant behaviour in energy efficient dwellings: evidence of a rebound effect

The energy required for space heating has been significantly reduced in recent decades by making use of insulation and more efficient heating and ventilation systems. Even so, wide variations in energy consumption are still observed between similar dwellings and between actual and predicted levels. It is thought that these variations stem from differences in occupant behaviour, the structural quality of the building, and a rebound effect. This paper statistically examines differences in occupant behaviour in relation to the building characteristics of the housing stock in the Netherlands and explores the possible existence of a rebound effect on the consumption of energy for space heating. Rebound effect can be defined as the increase on energy consumption in services for which improvements in energy efficiency reduce the costs. We found that although energy consumption is lower in energy efficient dwellings, analysis of the behaviour variables indicates their occupants tend to prefer higher indoor temperatures and to ventilate less. This finding might be related to a rebound effect on occupant behaviour. However, the improvement of thermal properties and systems efficiency still lead to a reduction on energy consumption for heating.     

Occupant related household energy consumption in Canada: Estimation using a bottom-up neural-network technique

A national model of residential energy consumption requires consideration of the following end-uses: space heating, space cooling, appliances and lighting (AL), and domestic hot water (DHW). The space heating and space cooling end-use energy consumption is strongly affected by the climatic conditions and the house thermal envelope. In contrast, both AL and DHW energy consumption are primarily a function of occupant behaviour, appliance ownership, demographic conditions, and occupancy rate. Because of these characteristics, a bottom-up statistical model is a candidate for estimating AL and DHW energy consumption. This article presents the detailed methodology and results of the application of a previously developed set of neural network models, as the statistical method of the Canadian Hybrid Residential End-Use Energy and Greenhouse Gas Emissions Model (CHREM). The CHREM estimates the national AL and DHW secondary energy consumption of Canadian single-detached and double/row houses to be 248 PJ and 201 PJ, respectively. The energy consumption values translate to per household values of 27.8 GJ and 22.5 GJ, and per capita values of 9.0 GJ and 7.3 GJ, respectively.     

Estimating space heating determinants: An analysis of Greek households

The purpose of this paper is to estimate the main determinants of households’ residential consumption for space heating in Greece employing cross-section data for 2003. In the empirical analysis household space heating consumption models are employed, using OLS, probit, tobit and quantile regression techniques. The results show that demographic and economic variables such as age of the respondent, family size and households’ annual income are suitable to explain differences towards oil consumption for space heating. In addition, the results suggest that dwelling's size and rate of occupancy are positively associated with the amount of oil that Greek households consumed in order to achieve a significant level of heat comfort for their house. By evaluating household's decision-making process with regard to space heating consumption we are able to propose an effective energy policy framework.     

Evaluating diverse patterns of occupant behavior regarding control-based activities in energy performance simulation

Simulation is recognized as an effective tool for building energy performance assessment during design orretrofit processes. Nevertheless, simulation models yield deviating outcomes from the actual building performance and a significant portion of this deviation originates from the dynamic nature of occupant behavior. Literature on occupant behavior indicates that occupant behavior is not integrated into building energy performance assessment procedures with appropriate resolution, instead they are acceptedas as sumedand fixed data sets that usually represent the presence of occupants. This study attempts to evaluate the effect of diverse patterns of occupant behavior on energy performance simulation for office buildings. Diverse levels of sensitivity of occupant behavior on control-based activities such as using lighting apparatus, adjusting thermostat settings, and presence in space are employed through three diverse occupant behavior patterns. These occupancy patterns are correlated with three identical office spaces simulated within a conceptual office building. EDSL Tas is used to run building energy performance simulations. Effects of occupant behavior patterns on simulation outcomes are compared for five sample winter and summer workdays, with respect to heating and cooling loads. Results present findings on how diversity of occupancy profiles influences the consumption outcomes.     

The impact of window opening and other occupant behavior on simulated energy performance in residence halls

A poor depiction of occupant behavior in building performance simulation frequently results in substantial divergences between real and simulated results. The problem may be of particular concern with simulation supporting the renovation of older multi-unit residential buildings, buildings whose occupants use windows for temperature control even during heating season. Here, we investigated the impact of window operation models (as well as other occupant behaviors) on simulated energy performance in university residence halls. Based on environmental monitoring, along with information collected from occupant surveys and wearable devices, we estimated air exchange rates and developed a probabilistic window-operation prediction model. The data were collected in 76 dormitory rooms sampled from a pre-renovated historic building and two similar buildings. We then evaluated the window-operation model’s predictive performance in 15 dormitory rooms in the post-renovated building with new occupants. The results of our predictive model were also compared with previously reported window-operation models. We implemented each window-operation model in a calibrated EnergyPlus building performance model, comparing the results of each simulation to metered hourly steam consumption. The impact of the different window operation models on simulated heating energy use was significant (annual error ranging from 0.2% to 10%). Our model demonstrated the highest capability of predicting window state (accuracy=85.8%) and steam use (NMBE=?0.2%); however, some previously published windowoperation models also produced satisfactory performance, implying that such models may be generalizable to some extent. The results suggest that data collected from somewhat ubiquitous indoor environmental quality sensors can glean insights into occupant behavior for building performance simulation. Furthermore, the energy impacts resulting from the variations in occupant behavior studied here were large enough to show that the choice of behavior model can have meaningful implications for real-world applications, such as estimating saving from heating and lighting system upgrades.     

Lighting energy consumption in ultra-low energy buildings: Using a simulation and measurement methodology to model occupant behavior and lighting controls

As building owners, designers, and operators aim to achieve significant reductions in overall energy consumption, understanding and evaluating the probable impacts of occupant behavior becomes a critical component of a holistic energy conservation strategy. This becomes significantly more pronounced in ultra-efficient buildings, where system loads such as heating, cooling, lighting, and ventilation are reduced or eliminated through high-performance building design and where occupant behavior-driven impacts reflect a large portion of end-use energy. Further, variation in behavior patterns can substantially impact the persistence of any performance gains. This paper describes a methodology of building occupant behavior modeling using simulation methods developed by the Building Energy Research Center (BERC) at Tsinghua University using measured energy consumption data collected by the University of Washington Integrated Design Lab (UW IDL). The Bullitt Center, a six-story 4831 m² (52,000 ft²) net-positive-energy urban office building in Seattle, WA, USA, is one of the most energy-efficient buildings in the world (2013 WAN Sustainable Building of the Year Winner). Its measured energy consumption in 2015 was approximately 34.8 kWh/(m²?yr) (11 kBtu/(ft²?yr)). Occupant behavior exerts an out-sized influence on the energy performance of the building. Nearly 33% of the end-use energy consumption at the Bullitt Center consists of unregulated miscellaneous electrical loads (plug-loads), which are directly attributable to occupant behavior and equipment procurement choices. Approximately 16% of end-use energy is attributable to electric lighting which is also largely determined by occupant behavior. Key to the building’s energy efficiency is employment of lighting controls and daylighting strategies to minimize the lighting load. This paper uses measured energy use in a 330 m² (3550 ft²) open office space in this building to inform occupant profiles that are then modified to create four scenarios to model the impact of behavior on lighting use. By using measured energy consumption and an energy model to simulate the energy performance of this space, this paper evaluates the potential energy savings based on different occupant behavior. This paper describes occupant behavior simulation methods and evaluates them using a robust dataset of 15 minute interval sub-metered energy consumption data. Lighting control strategies are compared via simulation results, in order to achieve the best match between occupant schedules, controls, and energy savings. Using these findings, we propose a simulation methodology that incorporates measured energy use data to generate occupant schedules and control schemes with the ultimate aim of using simulation results to evaluate energy saving measures that target occupant behavior.     

Combined space cooling and water heating system for Hong Kong residences

The combined space cooling and hot water preheating system that utilizes the rejected condenser heat is considered one of the most cost effective energy conservation measures. However, simultaneous consumption characteristics are absent in public domain. Questionnaire surveys have been conducted to obtain the relevant information from 126 households residing in high-rise public rental residential buildings in Hong Kong, achieving a confidence level of 95%. The candidate households were selected by a convenience sampling approach, and the questionnaire was constructed using either forced-choice format or in numeric response format. The data obtained were verified by correlation analysis. Data collected includes the occupancy pattern, the installed air-conditioner and water heater characteristics, and the utilization pattern of air-conditioning and hot water. Based upon the collected data and site measurements, hourly, daily and monthly heat recovery and hot water heating demand profiles were established, as well as the correlation between tap water and outdoor air temperatures. The combined profiles enable the evaluation of the feasible use of the combined system, and for future sizing of hot water storage tanks. The potential energy and fuel cost saving associated with the use of the proposed combined system for typical public rental housing in Hong Kong was estimated to be 50%.     

The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock

As a consequence of the improved quality of thermal properties of buildings due to energy regulations, overall energy use associated with building characteristics is decreasing, making the role of the occupant more important. Studies have shown that occupant behaviour might play a prominent role in the variation in energy consumption in different households but the extent of such influence is unknown. The impact of the building's thermal characteristics on space heating demand has been well studied. There is however, little work done that incorporates the impact of consumer behaviour. This study aims to gain greater insight into the effect of occupant behaviour on energy consumption for space heating by determining its effect on the variation of energy consumption in dwellings while controlling for building characteristics. The KWR database from the Ministry of Housing in the Netherlands was used. This study showed that occupant characteristics and behaviour significantly affect energy use (4.2%), but building characteristics still determine a large part of the energy use in a dwelling (42%). Further analysis showed that some occupant behaviour is determined by the type of dwelling or HVAC systems and, therefore, the effect of occupant characteristics might be larger than expected, since these determine the type of dwelling.     

Comparing the energy performance of an electrochromic window under various control strategies

Electrochromic glazing can involve substantial energy consumption in the building sector with their optical properties driven either by various occupant needs or environmental conditions. This paper presents comparison of ten different control strategies developed for such a glazing system in terms of specific energy consumption of a PASSYS test cell where the glazing was installed. The control strategies include scheduled ON–OFF controllers, a PID and advanced fuzzy controllers which were developed using experimental data. We used energy simulation to analyse the annual heating, cooling and lighting demands investigating the influence of each control strategy to the thermal behaviour of the building.     

我国住宅建筑节能潜力分析——除供暖外的住宅建筑能耗

城镇民用建筑能源消耗分为北方地区供暖能耗、除供暖外的住宅能耗、除供暖外的一般性非住宅民用建筑和大型公共建筑能耗四部分,着重分析了其中的除供暖外的住宅能耗。首先,阐述了我国住宅建筑能耗的现状,指出目前我国住宅建筑能耗水平较低,但呈现加速增长的趋势;其次,针对我国住宅建筑能耗的刚性增长趋势,提出了住宅建筑节能的重点——住宅空调器升级与更新、降低照明能耗和减少家电待机损失。并分析计算了三个方面的节能措施所能达到的实际节能效果。     

Contrastive analyses on annual energy consumption characteristics and the influence mechanism between new and old residential buildings in Shanghai, China, by the statistical methods

The purposes of this research are to contrast the energy use characteristics of old residential buildings and new residential buildings in Shanghai, China, to look into influence factors of residential energy consumption, and to further analyze the reasons which result in the differences of energy consumption quantities between high-energy use family group and low-energy use family group. 1610 families in Residential District A and 819 families in Residential District B were chosen to trace their monthly energy consumption data in the whole year of 2006. Buildings in District A were all constructed in the 1980s, while those in District B were built in the 2000s. 300 families in each district were further selected from all above investigated families to do questionnaires in the year of 2007, so as to understand building characteristics, the possession and utilization of space heating and cooling appliances, and energy-saving consciousness. Annual energy consumption of the two kinds of buildings is contrasted and energy consumption quantities of spacing cooling and heating are also calculated. Influencing factors of residential energy consumption are analyzed by Quantification Theory I. Quantification Theory III is used to classify all the families into different categories based on the differences in their energy consumption amounts, and to further find out the reasons leading to the different energy consumption between different groups. Conclusions are as follows: (1) the average annual energy consumption quantity is 23.27 GJ/household for new buildings and 14.40 GJ/household for old buildings. The ratio of space heating and cooling to total annual energy consumption is just 16% and 11.6% for new buildings and old buildings respectively; (2) energy consumption and its variance lie on the integration of many factors, such as the floor area, materials of window frames, the number of family members, operation months of space heaters in winter and air conditioners in summer, and energy-saving actions; (3) all the families in the two districts can be classified into two categories: Household Region M of much energy use, and Household Region N of little energy use. Adopting the aluminum window frames, large floor areas and the large number of family members (above 4 person) are the main reasons leading to more energy use in Household Region M, while the small number of family members (1-2 persons/household) and small floor areas are the main reasons resulting in the less energy use in Household Region N; the long period of space heating, using illumination as little as possible are also the reasons causing the differences in energy consumption quantities between the two categories, but their influences on the samples clustering are smaller than the main reasons above; (4) compared with the energy consumption in some developed countries, the ratio of space heating and cooling to total residential energy use is much smaller in Shanghai. Indoor thermal environment is very poor besides that. With the growth of economy and the improvement of living standard, people will have the higher requirement for good-quality indoor thermal environment, and hence space heaters and coolers will be used much more frequently, so the residential energy consumption in China will still continuously increase rapidly, if few energy-conservation strategies are adopted; (5) considering current little prevalence of energy-saving actions with low efficiency, more effective energy-saving actions should be fully adopted in China.     

Modeling occupancy and behavior for better building design and operation—A critical review

People spend more than 90% of their life time in buildings, which makes occupant behavior one of the leading influences of energy consumption in buildings. Occupancy and occupant behavior, which refer to human presence inside buildings and their active interactions with various building system such as lighting, heating, cooling, ventilation, window blinds, and plugs, attract great attention of research with regard to better building design and operation. Due to the stochastic nature of occupant behavior, prior occupancy models vary dramatically in terms of data sampling, spatial and temporal resolution. This paper provides a comprehensive review of the current modeling efforts of occupant behavior, summarizes occupancy models for various applications including building energy performance analysis, building architectural and engineering design, intelligent building operations and building safety design, and presents challenges and areas where future research could be undertaken. In addition, modeling requirement for different applications is analyzed. Furthermore, a few commonly used statistical and data mining models are presented. The purpose of this paper is to provide a modeling reference for future researchers so that a proper method or model can be selected for a specific research purpose.     

Optimization of the management of building stocks: An example of the application of managing heating systems in university buildings in Spain

The article presents the implementation of management measures that have reduced the gas consumption for space heating of a university building in Spain by 40%. The measures affect the use of the building, e.g., the use of its spaces, and the scheduling of its occupancy. They also affect the management of the heating system: a protocol for turning the system on and shutting it off has been developed.The work is part of a framework for action by the Technical University of Catalonia covering more areas and for a longer period of time, in which policies have been designed for managing buildings and a number of studies have been developed on the energy consumption of the university's buildings. The article highlights the work methodology proposed to achieve the energy savings calculated theoretically in a previous study. The implementation of these measures in the case, from November 2006 until May 2007, has reduced consumption from 113.2 kWh/m² to 68.7 kWh/m² a year; thus, the hypothesis has been validated. In addition, it was possible to examine methodological details more closely. Finally, the relationship between the people involved in the process (the building owners, managers and energy users) is a determining factor.     

论山西雁北地区住宅节能

介绍了住宅能耗的构成和．采暖能耗的损失途径,结合相关政策,针对山西罹北地区气候特点,探讨了该地区住宅节能方法,着重阐述了降低采暖能耗的具体措拖,以期促进雁北地区住宅节能工作。     

An agent-based stochastic Occupancy Simulator

Occupancy has significant impacts on building performance. However, in current building performance simulation programs, occupancy inputs are static and lack diversity, contributing to discrepancies between the simulated and actual building performance. This paper presents an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity. Each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastic behaviors. The occupancy behaviors are represented with three types of models: (1) the status transition events (e.g., first arrival in office) simulated with probability distribution model, (2) the random moving events (e.g., from one office to another) simulated with a homogeneous Markov chain model, and (3) the meeting events simulated with a new stochastic model. A hierarchical data model was developed for the Occupancy Simulator, which reduces the amount of data input by using the concepts of occupant types and space types. Finally, a case study of a small office building is presented to demonstrate the use of the Simulator to generate detailed annual sub-hourly occupant schedules for individual spaces and the whole building. The Simulator is a web application freely available to the public and capable of performing a detailed stochastic simulation of occupant presence and movement in buildings. Future work includes enhancements in the meeting event model, consideration of personal absent days, verification and validation of the simulated occupancy results, and expansion for use with residential buildings.