Methods for determining the energy performance of buildings should be verified so that they can be an effective tool in the process of developing energy-efficient solutions and be helpful in shaping energy policy. This paper compares the set of calculated values from the Polish Central Energy Performance of Buildings Registry with the values measured in buildings before and after thermal renovation, published by the Statistics Poland, as well as the data estimated by cooperation of the Statistics Poland and the Polish Government. The analysis showed that estimated and calculated values understate the average energy consumption for heating purposes. The mean measured energy consumption is 12% and is 5% higher than the mean calculated energy consumption and the estimated energy consumption respectively. The dispersion of measured values is 2.3 times larger than the dispersion of calculated values. It was also shown that, acc. to the analyzed database of thermally renovated buildings, the mean energy consumption of thermally renovated buildings decrease by 25% and the dispersion of annual energy consumption values decrease 1.6 times. This paper confirms the thesis that the current Polish methodology of building energy performance calculation needs a continuous and systematic verification or replacement by other tools guaranteeing the increase of national energy efficiency.
The increasing consumption of fossil fuel energy has led to resource constraints and climate change. This in turn has motivated intensive research into reducing the energy intensity of processes and increasing the possibilities of renewable energy sources usage. This type of research is also being carried out in the field of broadly defined buildings and related energy systems. The aim of buildings is to maintain a healthy and comfortable indoor environment for residents. Therefore the effort to reduce energy intensity is faced with the needs and expectations of building users, who use the energy supplied to the building directly. Results of the research in recent years have shown that the energy performance of buildings, especially energy efficient ones, strongly depends on their usage. Therefore a number of international projects are in progress to improve building occupancy models and also, inter alia, to find better system solutions, design methods and control devices which would allow to reduce energy intensity of buildings while maintaining high comfort of living and working in their spaces. In the last decade, the number of research conducted on this topic has increased significantly. More models of occupants are being developed, including factors such as occupants presence in a building, opening windows, shading blinds or using artificial lighting. These researches show that user behavior is a complex, multi-factor process difficult to analyze. There is still no universal model of occupant behavior. In turn the measurement process requires an advanced measurements campaign, which should be conducted in an existing, occupied building (not a laboratory). The problem is even more difficult in residential buildings, where such a measurements may disturb the privacy and tranquility of the occupants. Another way to study human-building interactions is to use statistical techniques to analyze a limited set of observed variables, but collected for a larger number of facilities. However, these are often annual aggregated data, which makes more detailed analysis difficult. It is also worth mentioning that the research conducted so far on residential building occupant behavior is limited to only a few countries. The issue addressed in this thesis is modeling building occupants behavior and analyzing its influence on energy use in Polish conditions. The research data includes daily records from residential meters for heat, water, gas and electricity, as well as surveys answered by nearly 50\% of the studied households. The data is easily accessible, it does not require entering dwellings, so it can be obtained without disturbing privacy of residents. This thesis presents methods for data analysis and the resulting information. Such methods can be applied to many objects. Therefore, the presented approach can be used on a wider scale. The research was conducted in a large city (about 500 thousand citizens) on a housing estate of municipal buildings erected at the beginning of the 21st century. Due to the regulations existing at that time, the investigated buildings have insulated external walls, that allows them to be classified as energy efficient buildings. The data covers approximately 100 apartments. Among the conducted measurements, the longest one was the measurement of heat consumption (III 2015 - II 2016). The analysis was based also on meteorological data measured in the same city. In this thesis, solar heat gains were determined for each apartment for the studied period using TRNSYS software, which is dedicated to dynamic energy analysis of buildings and systems. The statistical analysis of the collected data and the development of user models were carried out in the software R, with the use of, among others: correlation analysis, linear regression and k-means and hierarchical algorithms for grouping elements of the set by many features. The value of theoretical heat demand for space heating was calculated as a reference value for the analyses. For this purpose the monthly method was used, which is applied for determining the energy characteristics of buildings according to the Polish legal regulations. The total ventilation and infiltration flow assumed for these calculations was determined based on the hygienic criterion (concentration of CO2 ≤ 1000 ppm in the indoor air). The conducted analyses have shown that on average, the investigated apartments consume approximately two times more heat for space heating than it results from theoretical calculations. However, analyzing separately the individual apartments one can observe both apartments with definitely lower and definitely higher heat demand than the theoretical one. The thesis also analyzes the influence of solar radiation and external temperature on the ventilation intensity and shows the resulting seasonal variation in the coefficient of heat gain utilization. The most significant achievements of the work are: • the method for separating heat usage for domestic hot water and space heating from the total heat meter record; • the model of domestic hot water consumption in the form of a clustering of households by weekly consumption schedule; • the model of apartments occupancy in the form of a households clustering according to the amount and schedule of metabolic internal heat gains; • the estimate of the actual internal heat gains for each apartment based on conducted measurements; • the model of ventilation system control by occupants as a function of meteorological conditions, taking into account heat transfer with adjacent apartments; • the model of heating system control by occupants in the form of clustering of the radiator and its thermostatic valve adjustment strategy; • the measurement analysis of the heat gains utilization in apartments. Based on the results presented in this thesis, it can be concluded that the reduction of energy intensity of residential buildings, especially those characterized by a high population and energy-efficiency (whose thermal balance strongly depends on the occupancy schedule and occupants' actions), requires the improvement of the concept of heating and ventilation systems and their control method. New solutions should be tested by means of dynamic energy simulation with appropriate assumptions regarding the occupancy and occupant interaction with the building, its HVAC and electrical systems.
In order to gain a good understanding of residential building energy consumption in China, a case study about occupants' use of electricity for lighting, appliances and room air conditioners was carried out in 44 identical apartments in Beijing. Additionally, two apartments with significantly different energy consumption levels were chosen for a detailed study about the impact of occupants' daily behavior in using lighting, appliances and room air conditioners. The results of this study demonstrate the important contribution of occupant behavior to the significant variation of electricity consumption among residential buildings. The detailed measurement of occupant behavior showed a potential in better understanding how energy is consumed in buildings and relevant information gathered from this process can help occupants change their behavior for energy saving.
Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. The study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.
This study investigates human-building interaction in office spaces across multiple countries including Brazil, Italy, Poland, Switzerland, the United States, and Taiwan. We analyze social-psychological, contextual, and demographic factors to explain crosscountry differences in adaptive thermal actions (i.e. cooling and heating behaviors) and conformity to the norms of sharing indoor environmental control features, an indicator of energy consumption. Specifically, personal adjustments such as putting on extra clothes are generally preferred over technological solutions such as adjusting thermostats in reaction to thermal discomfort. Social-psychological factors including attitudes, perceived behavioral control, injunctive norms, and perceived impact of indoor environmental quality on work productivity influence occupants' intention to conform to the norms of sharing environmental control features. Lastly, accessibility to environmental control features, office type, gender, and age are also important factors. These findings demonstrate the roles of social-psychological and certain contextual factors in occupants' interactions with building design as well as their behavior of sharing environmental control features, both of which significantly influence building energy consumption , and thus, broader decarbonization.
Data was collected in the field, from an office building located in Frankfurt, Germany, over the period of 4 years. The building was designed as a low-energy building and featured natural ventilation for individual control of air quality as well as buoyancy-driven night ventilation in combination with a central atrium as a passive cooling strategy. The monitored data include in total 116 data points related to outdoor and indoor environmental data, energy related data, and data related to occupancy and occupant behaviour. Data points representing a state were logged with the real timestamp of the event taking place, all other data points were recorded in 10 minute intervals. Data were collected in 17 cell offices with a size of ~20 m², facing either east or west). Each office has one fixed and two operable windows, internal top light windows between office and corridor (to allow for night ventilation into the atrium) and sun protection elements (operated both manually and automatically). Each office is occupied by one or two persons.
This study employs a simplified Knowledge Discovery in Database (KDD) to extract occupancy, equipment and light use profiles from a database referred to 12 all-electric prefabricated dwellings in the Netherlands. The profiles are then integrated into a building performance simulation (BPS) model using the software TRNSYS v17. The significance of the extracted profiles is verified by comparing the total and end-use yearly electricity consumption of the investigated dwellings as predicted by the simulation tool with on-site measurements. For the considered dwellings, using standard OB modeling results in an underestimation of the energy use intensity (EUI) by 5.9% to 42.5%, depending on the case. The integration of the occupant behavior (OB) profiles improves the total electricity consumption prediction from an initial 22.9% average deviation from measurements to 1.7%. The results corroborate that the 1.6x discrepancy observed in the buildings’ energy use intensity could be entirely ascribed to OB. Then, the knowledge extracted from the households’ database is used to propose a local electricity market framework to reduce the electricity bill and grid dependency of all households. This study confirms the need for appropriate OB modeling in BPS, it shows the potential of the KDD method for successful OB profiles extraction, and is a first example of data-mined OB profiles integration in BPS, as well as of OB profiles deployment for a practical application other than energy use prediction.
This doctoral dissertation explores how computational modelling, simulation and sensitivity analysis techniques can be used to appropriately represent occupants and their behaviors when predicting the energy and comfort performance of buildings. More specifically, the aim of this work is to develop a computational methodology that can be used to facilitate the model selection of various OB aspects in order to achieve fit-for-purpose OB modelling. The methodology comprises four main steps. The first three steps (the Impact Indices Method, the Diversity Patterns Method, and the Mann-Whitney U test) are developed to identify which OB aspects are truly influential for the simulation output. These three steps can be seen as a sequence of increasingly complex sensitivity analyses. The fourth step concerns dealing with the influential OB aspects. The outcomes of this research show that the computational methodology presented here is able to produce BPS models that include an adequate (fit-for-purpose) representation of occupants and their behavior, while satisfying the purpose of the simulation in an efficient and effective way.
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This report presents the main findings of the activity 4.4 “Reference procedures for obtaining occupancy profiles in residential building” belonging to IEA EBC Annex 66 Subtask A. The activity was conducted with the aim of providing methods to describe occupancy in residential buildings and technical approaches to define occupancy profiles for energy simulations. Occupancy data can be classified into four levels: presence status (“occupied” and “unoccupied”), number of occupants, place in the space and activity. Occupants’ profiles can be defined by considering how people occupy the building, how they use the systems (heating, cooling, etc.), and how they interact with devices including windows, blinds, lights, appliances, etc. Occupancy profiles may differ significantly from each other and affect the energy performance of buildings. Their determination is essential as they are necessary inputs to energy building simulation. Based on these considerations, the report is focused on: 1. Investigating the procedures used to obtain occupancy profiles and their limitations; 2. Identifying the problems of data collection methods; 3. Characterizing different types of variables necessary to define representative occupancy profiles. To achieve the aims above, the following steps were done: 1. Completing a literature review by considering different residential context and doing a classification (by continent, methodology, type of statistical analysis and other); 2. Providing information about methodologies for data collection and processing; 3. Define the variables to be considered in surveys to get occupancy profiles.
An experimental set-up was built in an office with the aim of obtaining information regarding occupancy patterns by monitoring occupancy state, air temperature, relative humidity, CO2, VOC, door and window opening, and electricity usage. Heuristic approaches were applied: cluster analysis and models based on logical flowcharts. Cluster analysis was implemented in the ground truth occupancy data to identify daily occupancy patterns by considering different time steps. Clusters marked by daily occupancy lower and greater than 40% were identified. Furthermore, in high occupancy clusters, the analysis distinguished groups in which the day with the highest occupancy was lower or greater than 40%. The same approach was applied with continuous parameters to verify the ability of sensors to replicate the characteristics of each identified cluster. CO2 and power clusters showed similarities in the number of clusters, days in each cluster, and occupancy percentage. In addition, both continuous and binary variables were used in models based on logical flowcharts to describe hourly occupancy profiles. The best solution with one parameter returned an error of 12%, by using two parameters an error of 10%. Models with three parameters showed errors of less than 10%, accuracy did not improve significantly by adding the fourth parameter.
Energy poverty intertwines with the issues of energy inequality and energy justice, presenting a particular challenge for low-income households (LIHs). This study explores energy justice in the U.S. through the lens of several interconnected questions: Do energy assistance programs have adequate participation rates? How accessible are energy efficiency (EE) appliances? Are there different energy practices across income groups? How does time schedule of energy practices differ across income groups, and how is it connected to energy demand flexibility? Based on two representative data sets, this study finds that affordability and accessibility remain serious problems for LIHs. LIHs have lower participation rates in many EE programs and own fewer EE appliances and smart grid technologies. Additionally, thermostat control strategies are different across income levels. LIHs tend to set one fixed temperature, even when they own a programmable thermostat, which is less energy efficient. LIHs engage in more energy practices throughout the daytime than their counterparts and show the least pronounced morning and evening peaks, indicating a relatively inflexible schedule and barriers to accepting demand response programs. This study concludes with policy implications for making energy more affordable, accessible, flexible, and better for the environment, while being fair to those often underserved.
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The paper presents a review of the literature on the use of the questionnaire as a tool for collecting energy data in residential buildings. Numerous studies used the questionnaire to gather necessary information for different purposes. However, even using the same tool, the procedures differ in terms of distribution and compilation, contact with the interviewees and type of proposed questions. One hundred thirty-seven studies were considered in the review, including both scientific articles and surveys reports. The available works were analysed and classified according to the geographical origin, period, sample size, sample structure, data collection methods, response rate, data processing, and objectives of the investigation. In the 80% of the selected studies, the questionnaire is used as unique tool for data collection, albeit cases in which the questionnaire is coupled with other survey techniques, such as field measurements and time use surveys, have also been recorded. The in-person interview is the most common completion option in the sample and it is also the method that produces the highest response rate (77.6%). Generally, the collected data are subjected to statistical processing (over 80% of the cases). Regarding the objective of the surveys, the questionnaire is mainly employed for investigations on energy consumption and occupants’ behaviour, but also other purposes were identified. Overall, the revised sample and the experiences reported are largely varied and heterogeneous. The lack of a homogeneous methodology appears in the use of an extremely diversified terminology. Therefore, the codification of a reference method and the standardisation of the nomenclature would be desirable. It would be useful to define general guidelines to be followed when designing surveys by using questionnaires. The review provides some suggestions and guidance on the use of questionnaire, highlighting strengths and weaknesses, and represents a source of information for future researches focused on the energy performance of buildings.
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The aim of the study was to create and administer a questionnaire to collect data and obtain typical occupancy profiles of residential buildings. The survey was developed by considering previous experiences conducted in the University of Calabria since 2012, and it was distributed among 80 families via email, face-to-face and through social network. Different levels of occupancy (high, medium and low) and different sub-categories of high occupancy (morning, afternoon, and intermediate) were individuated by processing the gathered data. Buildings energy consumption was investigated with regard to occupancy categories, and correlations were found.
In recent years, both legislative instruments and market demand drive the construction industry towards high-performing, low-energy consuming buildings. However, without considering the human dimension, technologies alone do not necessarily guarantee high performance in buildings. Occupant behavior is a leading factor influencing energy use in buildings. To investigate and quantify the human dimension in a building's energy use, an international research study has been launched as part of project ANNEX66, organized by the International Energy Agency using an interdisciplinary framework. The framework is a synthesis of theories from building physics and social psychology including social cognitive theory, the theory of planned behavior, and the drivers-needs-actions-systems ontology for energy-related behaviors. As a research tool, an online survey was designed to collect crosscountry responses from office occupants among 14 universities within 6 countries from 4 continents. This paper introduces results and findings of the Hungarian data collection campaign conducted among 207 occupants in 6 universities across the country.
The present study aims at investigating the potential advantages of integrating inter-occupant diversity information into occupant behaviour models used in building performance simulation. To this end, the authors model the operation of windows by occupants in a monitored open-plan office at aggregate and individual levels. The models use indoor and outdoor temperature as well as the interaction of these variables to estimate the probability of opening and closing windows in the building located in Vienna, Austria. Subsequently, a number of existing and novel metrics serve to compare the predictive performance of the aggregate and individual models. In addition, a calibrated energy model of the office area incorporates the window operation models to evaluate their potential contribution to the reliability of building performance assessments. The results of this exploratory case study suggest that individual window operation models outperform the aggregate model in capturing the peak and variations of window operation across occupants. This resulted in a more reliable thermal comfort assessment in the free-running season. The individual models, however, overestimated peak heating demand, as compared with the benchmark value resulting from the actual window operations in a single year.
In building refurbishment projects, dynamic building simulation is popularly used to predict the energy-saving potential of various refurbishment scenarios. However, in this process, it is not clear whether occupant behaviour should be carefully modelled due to the lack of evidence about its impact on the prediction results. To answer this question, this study selected a UK public building and used dynamic building simulation to predict the energy-saving potential of common refurbishment measures, under various occupant behavioural conditions. The results revealed that for the case study building occupants’ heating behaviour has a significant impact on the predicted energy-saving potential of all evaluated refurbishment measures: when changing from passive heating users to active heating users, the energy-saving potential was nearly doubled. Although occupants’ window opening behaviour was not shown to be as important as heating behaviour for the refurbishment of the case study building, it has a specific influence on the refurbishment measure of increasing window layers: when windows are opened longer, the effectiveness of increasing window layers on promoting the building energy efficiency is decreased. According to the findings from this study, occupant behaviour should be considered as an important aspect in building refurbishment projects.
Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social, and local comfort parameters. In this study, thirty-three projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.
2018 Proceedings of 10th Windsor Conference: Rethinking Comfort. Previous studies have demonstrated diverse effects of different factors on occupant window behaviours. It is necessary to choose appropriate subsets of different behavioural window opening features, and to eliminate irrelevant and redundant features so as to avoid overfitting, noise and random fluctuations being learned by the model, and improve the accuracy of predictive models of window opening. The choice of protocols for the selection of features has been widely accepted as one of the most important steps in developing machine learning prediction algorithms. This study employed the use of both a recursive and a non-recursive feature selection method designed to consider all influencing factors simultaneously to explore the confounding effects inherent in various factors pertaining to the prediction of window opening behaviour. Two machine learning algorithms were applied as estimators in a recursive selection process, namely support vector classification (SVC), logistic regression (LR), and one in a non-recursive process, namely random forest (RF). Additionally, two processing schemes in the recursive method analysis were tried to determine the optimal feature subset based on corresponding algorithms, namely recursive feature elimination (RFE) and recursive feature elimination with cross validation (RFECV). Seven factors were considered in the feature selection process based on collected data, including: indoor temperature, outdoor temperature, relative humidity, concentrations of PM2.5, air quality index (AQI), wind speed and wind direction respectively. The results showed that different feature subsets can generate different prediction accuracy within the recursive method. RFECV can determine the most appropriate feature subset effectively with the consideration of the correlation among various factors. Both LR and SVC were proved to be effective as estimators embedded in RFECV, however SVC is more computationally expensive and LR shows a larger variance within the feature subset space. RF, as a non-recursive method, demonstrated real advantages in eliminating redundant features compared to the recursive feature selection process.
Energy performance contracting (EPC) aims at guaranteeing a specified level of energy savings in the built environment for a client. Among the building energy performance uncertainties that hinder EPC, occupant behavior (OB) plays a major role. For this reason, energy service companies (ESCOs) may be interested in including OB-related clauses in their contracts. The inclusion of such a clause calls for an efficient, easy-to-implement method to provide a first estimate of the potential effect of various aspects of OB on building cooling and heating energy demand. In contrast with common sensitivity analysis approaches based on a high number of scenarios, a novel simulation method requiring only a single simulation run for both heating and cooling seasons is presented here. The estimate is provided by evaluating the newly developed impact indices (II) based on the results obtained by means of the simulation run. A set of 16 building variants differing in floor height, climate, construction vintage and equipment and lighting power density was investigated to test the method. All II were calculated for the 16 building variants. In order to verify their significance, the results of a one-at-a-time sensitivity analysis mimicking simplified variations in occupant behavior (OB) were plotted against the II. The R2 values were above 0.9 when evaluating the effect of equipment use, lights use, and occupant presence, confirming the significance of the developed II. For blind use and temperature setpoint setting, the R2 values were ca. 0.85. Subsequently, the method was applied to an existing office building in Delft, The Netherlands, to evaluate its potential for EPC. This study confirms the high variability of the effect of OB on heating and cooling energy demand according to the case at hand. The developed method is useful for practitioners to evaluate the potential effect of OB on a given design in a time-effective manner.
Energy efficient buildings play an important role in achieving a sustainable society. Conventional methods achieving energy efficient buildings mainly focus on upgrading the physical properties of the building, such as increasing their thermal insulations, neglecting the occupants who are using the building. This study justifies the potential contribution of a new method, i.e. selecting offices for occupants with a consideration of their behavioural preferences and the building’s physical properties, to the building’s energy efficiency. Dynamic building performance simulation has been adopted for the justification, based on a case study building with a simple rectangular shape. The occupant window behavioural model was developed from field measured data in an office building and the up-to-date stochastic approach was used to predict the state of windows for the simulation. Simulation results clearly reflect that 1) building’s physical properties, such as window orientation, have impact on the thermal performance of the building; 2) occupant behaviour can also affect the thermal performance of the building; and 3) considering both occupant behavioural preference and building’s physical properties can promote building’s thermal performance, without requirement of changing occupant behaviour.
Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor PM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior.
This study presents results from an interdisciplinary survey assessing contextual and behavioral factors driving occupants’ interaction with building and systems in offices located across three different Mediterranean climates in Turin (Northern), Perugia (Central), and Rende (Southern) Italy. The survey instrument is grounded in an interdisciplinary framework that bridges the gap between building physics and social science environments on the energy- and comfort-related human-building interaction in the workspace. Outcomes of the survey questionnaire provide insights into four key learning objectives: (1) individual occupant’s motivational drivers regarding interaction with shared building environmental controls (such as adjustable thermostats, operable windows, blinds and shades, and artificial lighting), (2) group dynamics such as perceived social norms, attitudes, and intention to share controls, (3) occupant perception of the ease of use and knowledge of how to operate control systems, and (4) occupant-perceived comfort, satisfaction, and productivity. This study attempts to identify climatic, cultural, and socio-demographic influencing factors, as well as to establish the validity of the survey instrument and robustness of outcomes for future studies. Also, the paper aims at illustrating why and how social science insights can bring innovative knowledge into the adoption of building technologies in shared contexts, thus enhancing perceived environmental satisfaction and effectiveness of personal indoor climate control in office settings and impacting office workers’ productivity and reduced operational energy costs.
Occupant behaviour is now widely recognized as a major factor in the disparity between predicted and measured building performance. Stochastic models are a convenient way to model the rational, diverse and complex nature of occupant behaviour, including presence and adaptive behaviour. The FMI standard was used to co-simulate the building energy modelling program EnergyPlus and a multi-agent platform that contains stochastic models in an integrated environment. Using an office building as a case study, we show that data mining, through a correlation matrix and a principal component analysis, was an efficient way of investigating the cumulated influence of occupant behaviour on energy performance. The organisation of simulations was achieved using design of experiments in order to take into consideration multiple building configurations. This paper demonstrates how data mining of stochastic simulations can be used to identify the determinants that have the greatest influence on building energy needs.
Occupant sensing and data acquisition are essential elements for occupant behavior research. A wide range of different types of sensors has been implemented to collect rich information on occupants and their interactions with the built environment, such as presence, actions, power consumption, etc. This information establishes a foundation to study the physiological, psychological, and social aspects of occupant behavior. This chapter summarizes existing occupancy and occupant behavior sensing and data acquisition technologies in terms of field applications, and develops nine performance metrics for their evaluation. The reviewed technologies focus on both occupants’ presence and interactions with the built environment, and are grouped into six major categories: image-based, threshold and mechanical, motion sensing, radio-based, human-in-the-loop, and consumption sensing. This chapter provides an overview and discussion of different current state-of-the-art and future sensing technologies for researchers.
Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security. However, the ability to discern the actual number of people in a room is beyond the scope of most current sensing techniques. To address this issue, a complex environmental sensor network is deployed in the Robert L. Preger Intelligent Workplace (IW) at Carnegie Mellon University. The results indicate that there are significant correlations between measured environmental conditions and occupancy status. It is shown that an average of 83% accuracy on the occupancy number detection was achieved by Gaussian Mixture Model based Hidden Markov Models during testing periods. To illustrate the consequent energy impact based on the occupant behaviour detection (i.e. number and duration of occupancy) in the space, an EnergyPlus model of the IW with an assumed standard variable air volume (VAV) system is created. Simulations are conducted to compare the energy consumption consequences between a prescribed occupancy schedule according to the ASHRAE 90.1 base case with the predicted occupancy behaviour. The results show that energy saving of 18.5% can be achieved in the IW while maintaining indoor thermal comfort.
This chapter provides guidance for survey development related to building occupant research. Many researchers studying occupant behavior have used survey methods to collect self-reported data of occupant behaviors in buildings, either exclusively or in tandem with data gathered in field or laboratory studies. The chapter also serves as a how-to guide for issues such as: (a) how should survey questions be conceptualized, (b) are the questions measuring what was intended, (c) how should questions be written so that participants understand the intent, (d) how can the validity be increased for the survey itself, (e) how does one select the appropriate sample for a survey, and (f) how should one select the appropriate survey tool for data collection? Real examples of occupant behavior survey research and case studies offer lessons learned and precedent for future research efforts. Finally, the last section of the chapter presents a brief discussion of interview methods.
There are numerous methods of collecting occupant-related data for the purpose of researching building occupants, each with its own strengths and weaknesses. The objective of this chapter is to guide the decision-making process for researchers who are about to embark on a new occupant data collection campaign. This chapter introduces Chaps. 6–8 by overviewing four methods for occupant research: in situ, laboratory, survey, and virtual reality. For each method, the advantages and disadvantages are laid out based on findings in the literature and the authors’ experiences. Next, a comprehensive list of occupant-related phenomena of interest is provided, along with a qualitative discussion of the merits of each data collection method for studying them. Finally, mixed methods research approaches—whereby multiple, complementary approaches are adopted in a single study—are briefly discussed. Following this chapter, the reader is presented with three chapters that provide recommended best practice for each of in situ (Chap. 6), laboratory (Chap. 7), and survey (Chap. 8) methods to researching occupants in occupants.
To communicate IEA-EBC’s R&D in buildings and communities to broader audience especially the academic and research communities, this special issue of Energy and Buildings highlights thirteen EBC Annexes. Operating agents and technical leads of the annexes contributed thirteen articles, summarizing research problems, research methods, technical activities, and main outcomes, by drawing some content from the annex’s technical reports, final reports, related journal articles and other publications.
Previous studies show that individuals' energy consumption tends to outweigh the technical efficiency gains. Occupancy behavior accounts for about 30% of the variance in overall heating consumption and 50% in cooling consumption. In addition, overall energy savings of 10-20% due to simple behavioral adjustments are a reasonable expectation. Unfortunately, there are few studies have focused on the specific cases of behavior in low-income houses, where unique individual energy behavior, demographic and socio-economic factors come into play. This paper investigates energy related occupancy behavior in low-income families through real-time power, indoor environment and occupancy presence measurement. Four residential houses with different building envelope materials are used as test-beds. Occupancy behavior includes thermostat schedules, occupancy presence and major appliance usages. A simulation study is further conducted to show the energy impact of occupancy behavior.
More than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.
Goal of the research is to assess evaluations of the innovative smart monitoring system Energy@home for domestic electricity consumption. Aim of the Energy@home system is to provide householders with a persuasive tool that allows to manage energy consumption more efficiently. A combination of persuasive communication strategies such as graphical real-time and historical feedbacks to encourage competitiveness against “similar” households are provided to users through domestic user-friendly interfaces and combined with personalized energy saving prompts sent via newsletters. The Energy@home system was tested on 52 users selected all over Italy. From the qualitative standpoint, the system was evaluated easy to use and useful from 95% of trial users. The average system evaluation on a 1-to-10 scale was 7.8. From the quantitative standpoint, the Energy@home system motivated domestic consumer to save more than 9% in the electricity bill and emerged as an effective tool in reducing stand-by consumption on average above 15%.
Occupant behavior (OB) is among the most influential causes of uncertainty in building performance predictions. Not considering the potential influence of uncertainties on building performance could mislead the decision-making process during the design phase. Hence, it is crucial to include the modeling of uncertainties within building performance simulation (BPS) models. As for uncertainties due to OB, there are a number of ways to model them in BPS tools, characterized by different levels of complexity. Literature shows that the appropriate modeling approach depends on the object and purpose of the simulation, which makes it difficult for the simulationist to favor a method over another a priori. Moreover, there are currently no guidelines which offer support for selecting the most appropriate modeling approach. This study seeks to fill this gap by proposing a step-by-step strategy – the fit-for-purpose occupant behavior modeling (FFP-OBm) strategy – to select the appropriate OB modeling complexity.
The “human dimensions” of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This paper presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building’s life cycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights. Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners; enhanced comfort conditions and productivity for building occupants; more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.
Based on the long-term field measurement of the use of air conditioners in a residential area of five households in Beijing, this paper analyses the bulk of data and discusses indoor and outdoor temperature, indoor humidity, personal preferences, retention time of indoor refrigeration capacity amongst other effects resulting from use of air conditioners. It identifies two modes in extremities: the most energy saving one and the most energy consuming one. By comparing with field measurement of energy consumption, it contends that the actual energy consumption when air conditioners in use is much higher than the most energy-saving mode, testifying to the possibilities to reduce refrigeration capacity consumption by adjusting users' behaviours with remarkable significance.
Energy building simulations are key tools to be used in each design phase of the building . Actually, the ability of building energy simulation programs to evaluate real energy use is undermined by a poor representation of the stochastic variables entering the calculation: among these, the ones relate human interactions with the control of the indoor environment are crucial.One of the reason of the discrepancy between simulated and real energy use in buildings lies in the fact that simulation tools are only able to describe control actions modelling deterministically and following predefined and fixed schedules. Indeed models of human behaviour are generally described by statistical algorithms that predict the probability of an action occurs.. Moreover, building energy simulation programs are not currently able to adequately evaluate different scenarios explaining occupant behaviour's influence. The efforts described in this paper are defining realistic user profiles to implement in most of the buildings simulation tools. This work mainly focused on the investigation on how different probabilistic user patterns (adjusting thermostat set-point and window opening behaviour) influenced energy consumption with the aim to compare the obtained results with a regular use of the simulation program. Models of human behaviour based on measurements were implemented and simulations results given as probabilistic distribution of values of energy consumption were calculated, depending on different levels of users' behavior
Whilst deterministic models such as prescribed schedules and black-box controls, are the traditional methods for representing occupant behavior in building performance simulation (BPS), recent trends have seen more stochastic models emerging. However implementation of these models in BPS software has been sporadic. This paper presents a review of occupant behavior modelling capabilities of popular BPS programs to clarify this issue, and uses these results to examine the usefulness of the obFMU module, recently released under the remit of IEA-EBC Annex 66.
Residential water use has become a critical issue of the built environment due to drought and increasing water retail price in many regions around the world. However, there is limited research done to understand water use behavior in residential buildings. This paper presents data analytics and results from monitoring data of daily water use in 50 single-family homes in Texas, USA. Based on data analysis, residents’ regular water use patterns are investigated. The results help generate awareness of water use behavior and support further studies in clustering water use behavior patterns and developing water use models for simulation.
The paper presents preliminary simulation analysis of stochastic internal heat gains influence on multifamily buildings energy balance. Both existing and energy efficient object have been considered. Three stochastic algorithms have been investigated in different building cases, cases differ: energy efficient standard, envelope heat capacity, flats location in building shape. Analysis confirm that increase in envelope insulation implies increase of internal heat gains importance for flats energy performance. Although internal heat gains influence is less significant for building as a whole. It is important to remember that internal heat gains are only one of many processes dependent on occupant behavior. There is more occupant- building interaction that directly influence on indoor environment quality and energy consumption, thus deeper analysis is needed.
Purpose of Review The goal of this paper is to structure the vast amount of research on occupant behaviour published recently and to relate these to the question, how this body of research can lead to increased energy efficiency in buildings. Recent Findings The number and variety of (a) influencing factors included in investigations, (b) modelling approaches and their evaluation and (c) model implementations into building performance simulation environments (BPS) are increasing. Thereby, approaches combining elements from social sciences and engineering sciences are applied to the first two, but their benefit for BPS remains unexplored. Summary The field of research dealing with the understanding and modelling of occupant behaviour in the built environments is evolving rapidly. The findings can be used to increase the robustness of energy-efficient building concepts, to evaluate energy-efficient technologies and to develop respective control strategies. However, any application outside the research environment needs to be done with great cautiousness given the high number of unexplored influences underlying behavioural patterns.
This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by 1) calibrating one of its input variables ‘metabolic rate’, and 2) extending it by explicitly incorporating the variable RMOT (running mean outdoor temperature) that relates to adaptive thermal comfort. The analysis was performed with survey data (n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study at Hermitage Amsterdam museum in the Netherlands. The Predicted Mean Votes (PMVs) were calculated for 35 survey days using 1) an a priori assumed metabolic rate, 2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, 3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation towards extreme cool and warm sensations remains partly underestimated.
The selection of modelling and simulation methodologies plays an unprecedented role over the predictive accuracy of the data-driven occupant behaviour and presence models. In this study, different modelling and simulation formalisms emerging from the literature were introduced to represent occupant's presence and lighting use in building performance simulation (BPS) tools. Lighting use and occupancy models were developed and simulated in these formalisms by employing six months worth of observational data gathered in five private offices in an academic building. The modelling and simulation formalisms' ability to regenerate the patterns of the observational dataset were contrasted. When a discrete-event agent-based lighting use model was coupled with an agent-based occupancy model ― whereby the events were defined as an arrival or a decrease in the indoor light intensity ―, both the mean weekday lighting load and the frequency/timing of the light switch-on actions could be predicted more accurately.
In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.
Smart building management and control are adopted nowadays to achieve zero-net energy use in buildings. However, without considering the human dimension, technologies alone do not necessarily guarantee high performance in buildings. An office building was designed and built according to state-of-the-art design and energy management principles in 2008. Despite the expectations of high performance, the owner was facing high utility bills and low user comfort in the building located in Budapest, Hungary. The objective of the project was to evaluate the energy performance and comfort indices of the building, to identify the causes of malfunction and to elaborate a comprehensive energy concept. Firstly, current building conditions and operation parameters were evaluated. Our investigation found that the state-of-the-art building management system was in good conditions but it was operated by building operators and occupants who are not aware of the building management practice. The energy consumption patterns of the building were simulated with energy modelling software. The baseline model was calibrated to annual measured energy consumption, using actual occupant behaviour and presence, based on results of self-reported surveys, occupancy sensors and fan-coil usage data. Realistic occupant behaviour models can capture diversity of occupant behaviour and better represent the real energy use of the building. This way our findings and the effect of our proposed improvements could be more reliable. As part of our final comprehensive energy concept, we proposed intervention measures that would increase indoor thermal comfort and decrease energy consumption of the building. A parametric study was carried out to evaluate and quantify energy, comfort and return on investment of each measure. It was found that in the best case the building could save 23% of annual energy use. Future work includes the follow-up of: occupant reactions to intervention measures, the realized energy savings, the measurement of occupant satisfaction and behavioural changes.
Over the past four decades, a substantial body of literature has explored the impacts of occupant behaviour (OB) on building technologies, operation and energy consumption. A large number of data-driven behavioural models have been developed based on field data. These models lack standardization and consistency, leading to difficulties in applications and comparison. To address this problem, an ontology was developed using the Drivers Needs Actions and Systems (DNAS) framework. Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language schema, titled 'occupant behaviour XML' (obXML) which is a practical implementation of OB models that can be integrated into building performance simulation (BPS) programs. This paper presents a newly developed library of OB models represented in the standardised obXML schema format. This library provides ready-to-use examples for BPS users to employ more accurate occupant representation in their energy models. The library, which contains an initial effort of 52 OB models, was made publicly available for the BPS community. As part of the library building process, limitations of the obXML schema were identified and addressed, and future improvements were proposed. Authors hope that by compiling this library building energy modellers from all over the world can enhance their BPS models by integrating more accurate and robust OB patterns.
Occupants’ window opening behavior influences the performance of buildings significantly. Good window use can provide a comfortable indoor environment with a minimum energy use, while improper window use may result in bad indoor environment and may also waste great energy. This paper identifies improper window uses in an existing building in Beijing, China, based on a year-long longitudinal monitoring of occupants’ window opening behavior, together with important indoor and outdoor environmental variables. The findings from this study provide evidence that occupants’ window opening behavior in real buildings is required to be improved to promote the energy efficiency of the building.
Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.
The reliability of building performance simulation (BPS) predictions is impaired by a number of uncertainties, among which occupant behaviour (OB) plays a major role. Methods to relevantly model OB are essential to achieve energy efficient and comfortable buildings. This study contributes to the ongoing discussion concerning how to model OB in BPS. Specifically, a sensitivity analysis to various aspects of OB is used to assess the impact of using different levels of modelling complexity in the conceptual design phase. A method based on the statistical Mann–Whitney test is proposed to identify those aspects of OB that are influential for a performance indicator, and which might require a higher modelling complexity. Sixteen variants of an individual office constitute the case study. The results show how generalizations concerning robustness of a building typology to OB are not possible. Increasing modelling complexity does not necessarily lead to more accurate, or even to different results. © 2016 International Building Performance Simulation Association (IBPSA).
The aim of this paper is to advance the understanding of social-psychological and architectural parameters influencing occupant behaviour and resulting operational energy consumption in office settings. Existing knowledge on occupant behavior is reviewed from an architectural and socialpsychological perspective. The identified parameters are visually mapped using the framework of Integral Sustainable Design (ISD). Conclusions are drawn concerning the interrelationship and interaction of parameters and the direction of future interdisciplinary research.
Building simulation is most useful and most difficult in early design stages. Most useful since the optimisation potential is large and most difficult because input data are often not available at the level of resolution required for simulation software. The aim of this paper is to addresses this difficulty, by analysing the predominantly qualitative information in early stages of an architectural design process in search for indicators towards quantitative simulation input. The discussion in this paper is focused on cellular offices. Parameters related to occupancy, the use of office equipment, night ventilation, the use of lights and blinds are reviewed based on simulation input requirements, architectural considerations in early design stages and occupant behaviour considerations in operational stages. A worst and ideal case scenario is suggested as a generic approach to model occupant behaviour in early design stages when more detailed information is not available. Without actually predicting specific occupant behaviour, this approach highlights the magnitude of impact that occupants can have on comfort and building energy performance and it matches the level of resolution of available architectural information in early design stages. This can be sufficient for building designers to compare the magnitude of impact of occupants with other parameters in order to inform design decisions. Potential indicators in early design stages towards the ideal or worst case scenario are discussed.
Building simulation is a powerful way to evaluate the performance of a building. The quality of simulation results however strongly depends on the accuracy of simulation input data. Especially for weather data files and occupant behaviour it is difficult to obtain accurate data. This paper evaluates the variability of building simulation results with regards to different weather data sets as well as different heating and cooling set points for a residential building in Victoria, Australia. Thermal comfort according to ASHRAE Standard 55, final energy consumption and peak cooling and heating loads are assessed. Simulations have been performed with Energy-Plus, and weather data for a multi-year approach have been generated with the software Meteonorm. The results show that different weather files for the same location as well as different conditioning set points can influence the results by approximately a factor of 2.