Search

Among the various factors that affect building performance, occupant behavior (OB) has received more and more attention and has become a research hotspot in building design and operation in recent years. However, due to the remarkable randomness and diversity of OB in buildings, it is difficult to accurately describe its characteristics using a fixed schedule, which is a normal approach of most energy simulation tools. DeST, a whole building energy simulation tool offers the function of stochastic OB simulation so that the simulation results can be more in line with the actual situation. In the newly released DeST 3.0, the OB modules are developed based on the actual observed OB data and are divided into two parts: occupancy and occupant action modules. The occupancy module is based on the Markov chain, which simulates the occupant movements among the spaces inside and outside the buildings. The occupant action modules are proposed with an environment and event-driven stochastic infrastructure based on a three-parameter Weibull distribution, such as occupant shading, window, and air-conditioning (AC)/heating control behavior modules. This paper presents the OB modules development for coupled simulation in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption. Firstly, an overview of the current modelling efforts of OB is given, from the trend to the existing foundation. Secondly, the development of OB modules for coupled simulation in DeST 3.0 is introduced in-depth, including co-simulation of AC/heating control behavior, window behavior and shading behavior. Lastly, two case studies that realize coupled simulation using the developed modules are shown, one is the comparison between existing occupancy schedules from GB standards and the developed occupancy simulation, and the other is the comparison between the result of simulating with or without using AC/heating control behavior modules in two bedrooms of the residential building. [ABSTRACT FROM AUTHOR]

Highlights • A methodology is introduced to rank a set of buildings based on energetic performance of occupants. • Ranking is done in two levels, each one gives specific information about occupants of a single building. • The underlying factors that create the ranks are examined to give occupants clues on how to improve their rank and reduce energy consumption. Abstract Identifying the impacts of occupants on building energy consumption has become an important issue in recent years. This is due to the interrelationship of influencing factors such as urban climate, building characteristics, occupant behavior, and building services and operation, which makes it challenging to identify the role of occupants in energy consumption. The research problem in this study lies in the fact that the occupants of a building may not be cautious regarding energy savings, and there exists no ground to assess their energy consumption behavior. One solution is the development of a systematic comparison procedure between similar buildings. This paper introduces a new procedure for comparison between occupants of several buildings to show the rank of each building among others and suggest occupants on reducing their energy consumption and improving their rank. The proposed framework is developed based on multiple data-mining methods, including clustering, association rules mining, and neural networks. The proposed methodology is composed of two levels. The first considers the amount of energy usage by occupants after filtering effects unrelated to the occupant behavior. The second ranks the buildings in terms of achieved and potential savings during the time under investigation. To demonstrate the application, the methodology was applied on a set of monitored residential buildings in Japan. Results suggest that the proposed method enhances the evaluation of buildings' energy-saving potential by revealing the occupants' contribution. It also provides diverse and prioritized strategies to help occupants manage their energy consumption by revealing the building energy end-use patterns. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract Numerous occupant behavior models, typically stochastic ones, have been built to quantify the effect of occupants on building energy simulation, which however are supposed to go through a rigorous validation, or evaluation process to claim that they are qualified to be used in the application. Current contributions relevant with occupant behavior modeling in literature have been resorting to statistical metrics rather than those orienting on their application, causing the disputation in interpreting the accuracy of the occupant behavior models when they are actually used in simulation. Furthermore, the stochasticity of occupant behavior models necessitates statistical evaluation of the model accuracy, instead of the comparison between actual data and a single-run simulated data. This paper proposes a methodological framework for model evaluation from an application-oriented perspective, with lighting behavior as a case study. The metrics for evaluating lighting behavior models are identified according to different application scenarios, while the comparison between the measurement and simulation is done by introducing the statistical hypothesis testing. Analogous analyses on other behaviors can be conducted in the future, which altogether would foster more rigor and application significance in occupant behavior model evaluation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of occupant lifestyle patterns on the energy performance of the residential buildings with various building envelopes in different climate zones. For this purpose, an existing multi-family apartment building in Iran was selected as the experimental residential building and the validity of energy simulation was examined based on the real data of the building energy consumption. The energy demand of the building before and after renovation was simulated via EnergyPlus for different climate zones. The sensitivity analyses confirmed that the occupants’ behavior has a significant impact on the building's thermal energy usage especially in hot climate areas and can change the heating and cooling loads up to about 90%. Using the validated model, a parametric study was performed to investigate the influences of the occupant's behavior on the efficiency of the building envelope including window and cladding materials in different climate zones. The results show that the interaction between the occupants’ behaviors and envelope materials is significant in that the occupant behaviors can change the strategy of choosing the envelop material types. Fluctuation of thermal energy consumption caused by changing of window types can rise to 20% for this case study. Also, it is shown that underestimating the level of occupancy loads can exaggerate the effects of the envelope on the thermal energy consumption rate. Overall, this study emphasizes the importance of applying near actual user behavior data on energy simulation analyses performed for the studies of building sustainability and lifecycle assessment. [ABSTRACT FROM AUTHOR]

Occupant behavior is a major contributing factor to building energy consumption. Large discrepancies might occur when occupants with diverse types in terms of behavior patterns reside in an identical building. Moreover, it is vital to derive several kinds of typical occupant behavior that represent the assemblage of occupants in a simplified way as the reference for building designers and energy policy makers. This paper presents a preliminary research on occupant behavior categorization through the building energy simulation, where some typical occupant air-conditioning behavior patterns use in Chengdu, China have been derived from a large-scale questionnaire survey based on the energy consumption levels. These typical occupant behavior patterns could be used as the context for an overview of building energy in a community, as well as the evaluation of building energy-saving technology where occupants are involved. Further efforts are required in the algorithm of dividing occupant behavior into typical patterns, the validation of the classification results, and the analysis of other behaviors. [ABSTRACT FROM AUTHOR]

Traditionally, in building energy modeling (BEM) programs, occupant behavior (OB) inputs are deterministic and less indicative of real world scenarios, contributing to discrepancies between simulated and actual energy use in buildings. This paper presents a new OB modeling tool, with an occupant behavior functional mock-up unit (obFMU) that enables co-simulation with BEM programs implementing functional mock-up interface (FMI). The components detailed in the development of the obFMU include an overview of the DNAS (drivers-needs-actions-systems) ontology and the occupant behavior eXtensible Markup Language (obXML) schema, in addition to details on the creation of the obFMU that contains the co-simulation interface, the data model and solvers. To demonstrate functionality of the tool, three examples of occupant behaviors were simulated, including: (1) turning on and off lights, (2) opening and closing windows, and (3) turning on and off the air conditioners. The obFMU can be used via co-simulation with all building simulation programs that implement the FMI, thus users are not limited to a particular tool. Another advantage is the use of obXML schema to represent occupant behavior, standardize the description of occupant behavior enabling information exchange. [ABSTRACT FROM AUTHOR]

This paper introduces a Human and Building Interaction Toolkit (HABIT) for simulating the thermally adaptive behaviors and comfort of office occupants alongside building energy consumption. The toolkit uses the Building Controls Virtual Test Bed (BCVTB) to co-simulate a field-tested, agent-based behavior model with an EnergyPlus medium office model. The usefulness of the toolkit is demonstrated through a series of zone and building-level case study simulations that examine the wisdom of pairing local heating and cooling options with strategic thermostat set point offsets, judging from the energy, Indoor Environmental Quality (IEQ), and cost perspectives. Results generally suggest that trading efficient local heating/cooling options for whole space conditioning has both energy and comfort benefits, saving up to 28% of monthly HVAC energy while improving the acceptability of thermal conditions in a Philadelphia climate. Nevertheless, cost analysis shows that the fuel source of conserved energy must be considered – particularly in the case of personal heater use, which adds to electric plug loads and associated utility and CO 2 emissions cost penalties. Moreover, costs from even small changes in simulated occupant productivity tend to overwhelm energy costs, suggesting the need to improve the accuracy and precision of available productivity models across multiple seasons and climates. [ABSTRACT FROM AUTHOR]

Although it is widely acknowledged that occupants play a critical role in building energy consumption, the characteristics of occupants are not well-represented in building simulation. Many statistic and data-mining technologies have been applied to develop a reliable occupant model. In contrast, rather than attempting to develop the occupant model, this study aims to investigate the correlation between the occupant behavior and energy consumption based on a series of experiments. First, this paper discusses the randomness of the occupants’ presence and behavior. The degree of randomness was verified using a Normalized Cumulative Periodogram (NCP) based on a random walk hypothesis. In addition, the correlation between occupant and energy consumption was investigated using the wavelet coherence. This study shows that, while the occupants’ presence has a randomness, it is not strongly correlated to the energy consumption. The occupants’ active action to control a heating/cooling system (turn on/off) is correlated to the energy consumption. In contrast to the occupant's presence, the occupants’ active action does not follow the random walk, and it has no particular frequency. This means that it is difficult to predict the control action of occupants with a specific time interval. [ABSTRACT FROM AUTHOR]

Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead. [ABSTRACT FROM AUTHOR]

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs. [ABSTRACT FROM AUTHOR]

In previous literature, it has been reported that over 50% of all energy consumed in buildings occurs during non-working hours. Unfortunately, to the best of the authors’ knowledge, studies to date using field-collected data have only investigated energy consumed during periods of non-occupancy in non-domestic buildings and little is known regarding this quantity of energy in households. The work presented in this paper bridges this information gap and contributes to the literature by presenting a first look into this quantity in households. In a yearlong investigation of seven dormitories, hourly occupancy data and electricity consumption is combined and analyzed using numerous statistical methods to discover the amount of energy that is expended in unoccupied households. Across the seasons, the average household consumed between 27.5% and 31.5% of all energy while unoccupied. This quantity in individual rooms fluctuated from under 4% to over 80%. Differences in occupant behavior, along with time spent unoccupied, can explain the differences in this quantity among the households. Further, no meaningful relationship was found between total energy consumption and percentage of energy spent in empty rooms for individuals. High and low energy users both spent electricity while away from home in proportion to his/her consumption. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

• A conceptual framework of in-situ monitoring activities is proposed. • Meta-analysis and synthesis are conducted with 68 selected monitoring activities. • Previous papers have to some extent insufficient documentation of monitoring activities. • An empirical rule is proposed on setting of time interval and monitoring duration. • Research opportunities rest on high-rise buildings in hot humid zone. Occupant behavior has a significant impact on building energy consumption and sustainable development of the community. In-situ monitoring of occupant behavior is one of the most effective and widely used research methods. It collects data of occupant behavior using smart sensors in the natural environment and, if appropriately designed and applied, can effectively avoid bias in the results. However, previous studies have rarely discussed how to design and apply in-situ monitoring activities in residential buildings. This paper, through a comprehensive and critical literature review, aims to close the knowledge gap on in-situ monitoring of occupant behavior in residential buildings. Multiple review techniques were used. First, a conceptual framework of monitoring activities was proposed based on a narrative appraisal of related publications. Second, the body of literature was established through an exhaustive search of papers by Web of Science and Scopus, two popular search engines. In total, 68 monitoring activities from 74 journal papers were selected according to the inclusion criteria. Third, meta-analysis and meta-synthesis were applied to this body of literature under the conceptual framework to reflect the achievements of previous studies and to explore the challenges facing future research. Results show that previous studies had limited consideration of sampling methods, setting of time interval and monitoring duration, installation of sensors, and the impact of microclimate. Ignoring these issues would reduce the productivity of data collected from in-situ monitoring activities and thus bring bias into the results. To address such limitations, recommendations are given for the design procedure of in-situ monitoring activities. In addition, an empirical rule is proposed with regard to setting the time interval and monitoring duration. Possible areas of future research are also discussed, e.g. occupant behavior in high-rise residential buildings in hot humid zone. The findings of this paper should facilitate the application of in-situ monitoring in building energy research and familiarize future studies with regard to occupant behavior in residential buildings. [ABSTRACT FROM AUTHOR]

• A POE-integrated simulation method is proposed to address energy occupant behavior. • Opening windows and operating AC were found to co-exist which affects simulation accuracy. • The new method improves energy simulation accuracy by 14% for domestic areas. • The method improves energy simulation accuracy by 16% for total building areas. Occupant behavior plays a critical role in building energy consumption, particularly in residential buildings. However, occupant behavior is complicated and varies significantly from case to case. Also, energy modeling of high-rise buildings is far less explored than that of low- or medium-rise buildings. This paper aims to improve accuracy in building energy simulation by utilizing Post Occupancy Evaluation (POE) data to calibrate energy model. Drawing on a review of the literature of occupant behavior and building energy modeling, the paper provides a calibration method of integrating POE data into an energy model, which is demonstrated using a real-life typical 40-storey residential building in Hong Kong. The developed method addresses seven updated input parameters, namely, schedule, devices, air-conditioners, windows, lights, domestic hot water, and cooking. By comparing the two energy modeling processes, i.e. with and without POE input, and their resultant estimated energy consumption, the paper quantifies the impact of occupant behavior on building energy consumption. Annual metered data together with energy bills obtained from the POE were utilized to validate the models. The results show that the use of the developed POE-integrated method helped to improve accuracy in energy consumption prediction by 14% for the total building floor area and by 16% for the total residential area. From examining the impacts of the seven input parameters, the paper reveals the key energy use sensitive occupant behaviors within high-rise residential buildings in Hong Kong. These include: the number of residents in each unit; adoption of window type and split type of air-conditioners; window and air condition operation modes; cooking time on weekdays and weekends; and time spent on hot water showering. The developed method can assist building designers and services engineers to estimate building energy use more accurately and provides a scenario analysis tool for clients and facility managers to develop effective energy conservation strategies. [ABSTRACT FROM AUTHOR]

Abstract: Buildings worldwide account for a surprisingly high 40% of global energy consumption and produce an increasingly large carbon footprint. Future sustainability of the building sector is therefore strongly dependent on the installation of energy efficient technologies. However, even though these technologies are becoming more and more efficient, human behavior still plays a very important role in the overall building energy consumption. In this paper, a model for occupant behavior within the building in relation to energy consumption, along with a building energy consumption model is proposed based on stochastic Markov models. The energy consumption model is used to predict possible energy saving gains from building retrofitting projects. The objective is to ensure the return on investment from these projects. The obtained results demonstrate that the proposed energy consumption model learns occupant behavioral patterns from the building. Additionally, it reliably reproduces them, predicts the building energy consumption and identifies potential areas of energy waste. [Copyright &y& Elsevier]

• A prediction model of lighting and shading coupling control behavior is proposed. • The preferences and control habits of occupants were investigated and classified. • The indoor work plane illumination was predicted by a machine learning model. • The preference diversity of occupants greatly affect the prediction accuracy. Lighting control in office buildings is driven by occupant's demand for indoor light environment. The control behavior not only has a direct impact on occupants' visual comfort, but also relates with the building lighting energy consumption. However, due to the effect of glare, lighting control is often associated with shading adjustment. In this regard, this paper proposed a prediction model which can accurately describe the lighting and shading coupling control behavior by fully considering the difference and diversity of occupants. The light environment preferences and the usage habits of lighting and shading system of occupants were firstly investigated and classified by means of questionnaire. Markov model and log-logistic survival model were introduced to quantitatively describe the probability distribution of various shading and lighting control behaviors. On this basis, combined with the indoor workplane illumination prediction model, the behavior of occupant's lighting and shading coupling control can be predicted. By comparing the four models considering or not considering the diversity and coupling effect, it is found that the proposed coupling prediction model shows better performance, the maxium error rate is only 13.04% for the lighting energy consumption prediction. [ABSTRACT FROM AUTHOR]

The occupants’ health, comfort, and productivity are important objectives for green building design and operation. However, occupant behavior also has “passive” impact on the building indoor environment by generating heat, CO 2 , and other “disturbances”. This study develops an “indirect” practical data mining approach using office appliance power consumption data to learn the occupant “passive” behavior. The method is tested in a medium office building. The average percentage of correctly classified individual behavior instances is 90.29%. The average correlation coefficient between the predicted group schedule and the ground truth is 0.94. The experimental result also shows a fairly consistent group occupancy schedule, while capturing the diversified individual behavior in using office appliances. Compared to the occupancy schedule used in the Department of Energy prototype medium office building models, the learned schedule has a 36.67–50.53% lower occupancy rate for different weekdays. The heating, ventilation, and air conditioning (HVAC) energy consumption impact of this discrepancy is investigated by simulating the prototype EnergyPlus models across 17 different climate zones. The simulation result shows that the occupancy schedules’ impact on the building HVAC energy consumption has large variations for the buildings under different climate conditions. [ABSTRACT FROM AUTHOR]