@article {31306, title = {Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings}, journal = {Energy and Buildings}, volume = {174}, year = {2018}, month = {09/2018}, pages = {214 - 227}, abstract = {

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.

}, keywords = {AC usage benchmarking, Air-conditioning, Clustering analysis, KPIs, residential building, Use pattern}, issn = {03787788}, doi = {10.1016/j.enbuild.2018.06.035}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818307199https://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/plain}, author = {Jingjing An and Da Yan and Tianzhen Hong} } @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 {30301, title = {Comparison of typical year and multiyear building simulations using a 55-year actual weather data set from China}, journal = {Applied Energy}, volume = {195}, year = {2017}, month = {06/2017}, pages = {890-904}, abstract = {

Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.

}, keywords = {Actual weather data, building simulation, energy use, Multiyear simulation, Peak load , Typical year}, doi = {10.1016/j.apenergy.2017.03.113}, author = {Ying Cui and Da Yan and Tianzhen Hong and Chan Xiao and Xuan Luo and Qi Zhang} } @article {30300, title = {A Comparative Study on Energy Performance of Variable Refrigerant Flow Systems and Variable Air Volume Systems in Office Buildings}, journal = {Applied Energy}, year = {2016}, abstract = {

Variable air volume (VAV) systems and variable refrigerant flow (VRF) systems are popularly used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their air conditioning energy use. Site survey and field measurements were conducted to collect data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data, normalized by climate and operating hours, show that VRF systems consumed much less air conditioning energy by up to 70\% than VAV systems. This is mainly due to the different operation modes of both system types leading to much fewer operating hours of the VRF systems. Building simulation was used to quantify the impact of operation modes of VRF and VAV systems on cooling loads using a prototype office building in China. Simulated results show the VRF operation mode leads to much less cooling loads than the VAV operation mode, by 42\% in Hong Kong and 53\% in Qingdao. The VRF systems operated in the part-time-part-space mode enabling occupants to turn on air-conditioning only when needed and when spaces were occupied, while the VAV systems operated in the full-time-full-space mode limiting occupants{\textquoteright} control of operation. The findings provide insights into VRF systems operation and controls as well as its energy performance, which can inform HVAC designers on system selection and building operators or facility managers on improving VRF system operations.\  \ \ 


}, keywords = {building simulation, comparative analysis, energy performance, field measurement, Variable Air Volume (VAV) Systems, Variable Refrigerant Flow (VRF) Systems}, author = {Xinqiao Yu and Da Yan and Kaiyu Sun and Tianzhen Hong and Dandan Zhu} } @article {57084, title = {Comparison of Building Energy Modeling Programs: HVAC Systems}, year = {2013}, month = {08/2013}, author = {Xin Zhou and Tianzhen Hong and Da Yan} } @conference {2673, title = {Comparative research in building energy modeling programs}, booktitle = {China Annual HVACR Conference}, year = {2012}, month = {06/2011}, address = {China (in Chinese)}, keywords = {advanced building software: energyplus, building energy modeling program, building simulation, comparison, dest, doe-2, energyplus, simulation research group, test}, author = {Dandan Zhu and Tianzhen Hong and Da Yan and Chuang Wang} } @conference {3404, title = {A Comparison of DeST and EnergyPlus}, booktitle = {China HVAC Simulation Conference}, year = {2011}, month = {2011}, address = {Beijing}, keywords = {building simulation, comparison, dest, energy modeling, energyplus, simulation research, simulation research group, test cases}, author = {Dandan Zhu and Chuang Wang and Da Yan and Tianzhen Hong} }