@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 {29897, title = {A Novel Stochastic Modeling Method to Simulate Cooling Loads in Residential Districts}, year = {2017}, abstract = {

District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized;this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2)occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution,and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

}, author = {Jingjing An and Da Yan and Tianzhen Hong and Kaiyu Sun} } @article {30312, title = {Spatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings}, journal = {Energy and Buildings}, year = {2017}, abstract = {

Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. The proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.

}, keywords = {air handling unit, chiller plant, equipment sizing, internal heat gain, spatial distribution, spatial diversity, stochastic}, doi = {10.1016/j.enbuild.2017.01.044}, author = {Qi Zhang and Da Yan and Jingjing An and Tianzhen Hong and Wei Tian and Kaiyu Sun} } @article {30303, title = {A Thorough Assessment of China{\textquoteright}s Standard for Energy Consumption of Buildings}, journal = {Energy and Buildings}, year = {2017}, month = {03/2017}, abstract = {

China{\textquoteright}s Design Standard for Energy Efficiency of Public Buildings (the Design Standard) is widely used in the design phase to regulate the energy efficiency of physical assets (envelope, lighting, HVAC) in buildings. However, the standard does not consider many important factors that influence the actual energy use in buildings, and this can lead to gaps between the design estimates and actual energy consumption. To achieve the national energy savings targets defined in the strategic 12th Five-Year Plan, China developed the first standard for energy consumption of buildings GB/T51161-2016 (the Consumption Standard). This study provides an overview of the Consumption Standard, identifies its strengths and weaknesses, and recommends future improvements. The analysis and discussion of the constraint value and the leading value, two key indicators of the energy use intensity, provide insight into the intent and effectiveness of the Consumption Standard. The results indicated that consistency between China{\textquoteright}s Design Standard GB 50189-2015 and the Consumption Standard GB/T51161-2016 could be achieved if the Design Standard used the actual building operations and occupant behavior in calculating the energy use in Chinese buildings. The development of an outcome-based code in the U.S. was discussed in comparison with China{\textquoteright}s Consumption Standard, and this revealed the strengths and challenges associated with implementing a new compliance method based on actual energy use in buildings in the U.S. Overall, this study provides important insights into the latest developments of actual consumption-based building energy standards, and this information should be valuable to building designers and energy policy makers in China and the U.S.

}, keywords = {China, code and standard, energy consumption, energy efficiency, Energy Use Intensity, outcome-based code}, doi = {10.1016/j.enbuild.2017.03.019}, author = {Da Yan and Tianzhen Hong and Cheng Li and Qi Zhang and Jingjing An and shan Hu} }