@article {30490, title = {Comparative Study of Air-Conditioning Energy Use of Four Office Buildings in China and USA}, journal = {Energy and Buildings}, volume = {169}, year = {2018}, abstract = {

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. This study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. Overall, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.

}, keywords = {Building envelope, climate, energy consumption, occupant behavior, office buildings, technological choice}, doi = {10.1016/j.enbuild.2018.03.073}, author = {Xin Zhou and Da Yan and Jingjing An and Tianzhen Hong and Xing Shi and Xing Jin} } @article {30482, title = {IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings}, journal = {Energy and Building}, volume = {156}, year = {2017}, abstract = {

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.

}, keywords = {building performance, energy modeling, energy use, IEA EBC Annex 66, Interdisciplinary approach, occupant behavior}, doi = {10.1016/j.enbuild.2017.09.084}, author = {Da Yan and Tianzhen Hong and Bing Dong and Ardeshir Mahdavi and Simona D{\textquoteright}Oca and Isabella Gaetani and Xiaohang Feng} } @article {30315, title = {Ten Questions Concerning Occupant Behavior in Buildings: The Big Picture}, journal = {Building and Environment}, year = {2017}, abstract = {

Occupant behavior has significant impacts on building energy performance and occupant comfort. However, occupant behavior is not well understood and is often oversimplified in the building life cycle, due to its stochastic, diverse, complex, and interdisciplinary nature. The use of simplified methods or tools to quantify the impacts of occupant behavior in building performance simulations significantly contributes to performance gaps between simulated models and actual building energy consumption. Therefore, it is crucial to understand occupant behavior in a comprehensive way, integrating qualitative approaches and data- and model-driven quantitative approaches, and employing appropriate tools to guide the design and operation of low-energy residential and commercial buildings that integrate technological and human dimensions. This paper presents ten questions, highlighting some of the most important issues regarding concepts, applications, and methodologies in occupant behavior research. The proposed questions and answers aim to provide insights into occupant behavior for current and future researchers, designers, and policy makers, and most importantly, to inspire innovative research and applications to increase energy efficiency and reduce energy use in buildings.

}, keywords = {Behavior Modeling, building performance, building simulation, energy use, interdisciplinary, occupant behavior}, author = {Tianzhen Hong and Da Yan and Simona D{\textquoteright}Oca and Chien-Fei Chen} } @article {60962, title = {Advances in research and applications of energy-related occupant behavior in buildings}, journal = {Energy and Buildings}, volume = {116}, year = {2016}, month = {03/2016}, pages = {694-702}, abstract = {

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.

}, keywords = {Behavior Modeling, Building design and operation, building performance simulation, energy use, occupant behavior}, doi = {10.1016/j.enbuild.2015.11.052}, author = {Tianzhen Hong and Sarah C. Taylor-Lange and Simona D{\textquoteright}Oca and Da Yan and Stefano P. Corgnati} } @article {59960, title = {Data Analysis and Stochastic Modeling of Lighting Energy Use in Large Office Buildings in China}, journal = {Energy and Buildings}, volume = {86}, year = {2015}, month = {01/2015}, pages = {275-287}, abstract = {

Lighting consumes about 20\% to 40\% of the total electricity use in large office buildings in China. Commonly in building simulations, static time schedules for typical weekdays, weekends and holidays are assumed to represent the dynamics of lighting energy use in buildings. This approach does not address the stochastic nature of lighting energy use, which can be influenced by occupant behavior in buildings. This study analyzes the main characteristics of lighting energy use over various timescales, based on the statistical analysis of measured lighting energy use data from 15 large office buildings in Beijing and Hong Kong. It was found that in these large office buildings, the 24-hourly variation in lighting energy use was mainly driven by the schedules of the building occupants. Outdoor illuminance levels had little impact on lighting energy use due to the lack of automatic daylighting controls (an effective retrofit measure to reduce lighting energy use) and the relatively small perimeter area exposed to natural daylight. A stochastic lighting energy use model for large office buildings was further developed to represent diverse occupant activities, at six different time periods throughout a day, and also the annual distribution of lighting power across these periods. The model was verified using measured lighting energy use from the 15 buildings. The developed stochastic lighting model can generate more accurate lighting schedules for use in building energy simulations, improving the simulation accuracy of lighting energy use in real buildings.

}, keywords = {building simulation, energy use, Lighting modeling, occupant behavior, office buildings, Poisson distribution, stochastic modeling}, doi = {10.1016/j.enbuild.2014.09.071}, author = {Xin Zhou and Da Yan and Tianzhen Hong and Xiaoxin Ren} } @article {59958, title = {Data Mining of Space Heating System Performance in Affordable Housing}, journal = {Building and Environment}, volume = {89}, year = {2015}, month = {07/2015}, pages = {1-13}, abstract = {

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. The results from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

}, keywords = {affordable housing, building simulation, clustering, data mining, decision tree, occupant behavior, space heating}, doi = {10.1016/j.buildenv.2015.02.009}, author = {Xiaoxin Ren and Da Yan and Tianzhen Hong} } @article {60948, title = {Occupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges}, journal = {Energy and Buildings}, volume = {107}, year = {2015}, month = {11/2015}, pages = {264-278}, abstract = {

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.

}, keywords = {building simulation, energy efficiency, energy modeling, energy use, occupant behavior}, doi = {10.1016/j.enbuild.2015.08.032}, author = {Da Yan and William O{\textquoteright}Brien and Tianzhen Hong and Xiaohang Feng and H. Burak Gunay and Farhang Tahmasebi and Ardeshir Mahdavi} } @article {59961, title = {Simulation of Occupancy in Buildings}, journal = {Energy and Buildings}, volume = {87}, year = {2015}, month = {01/2015}, pages = {348-359}, abstract = {

Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2{\textendash}4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.

}, keywords = {building simulation, co-simulation, occupancy, occupant behavior, software module, stochastic modeling}, doi = {10.1016/j.enbuild.2014.11.067}, author = {Xiaohang Feng and Da Yan and Tianzhen Hong} } @article {56249, title = {Data Analysis and Modeling of Lighting Energy Use in Large Office Buildings}, year = {2013}, abstract = {

Lighting consumes about 20 to 40\% of total electricity use in large office buildings in the U.S. and China. In order to develop better lighting simulation models it is crucial to understand the characteristics of lighting energy use. This paper analyzes the main characteristics of lighting energy use over various time scales, based on the statistical analysis of measured lighting energy use of 17 large office buildings in Beijing and Hong Kong. It was found that the daily 24-hour variations of lighting energy use were mainly driven by the schedule of the building occupants. Outdoor illumination levels have little impact on lighting energy use in large office buildings due to the lack of automatic daylighting controls and relatively small perimeter areas. A stochastic lighting energy use model was developed based on different occupant activities during six time periods throughout a day, and the annual distribution of lighting power across those periods. The model was verified using measured lighting energy use of one selected building. This study demonstrates how statistical analysis and stochastic modeling can be applied to lighting energy use. The developed lighting model can be adopted by building energy modeling programs to improve the simulation accuracy of lighting energy use.

}, keywords = {building simulation, energy use, lighting, modeling, occupant behavior, office buildings, Poisson distribution}, author = {Xin Zhou and Da Yan and Xiaoxin Ren and Tianzhen Hong} }