@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 {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} } @article {59969, title = {Integrated Design for High Performance Buildings}, year = {2014}, author = {Tianzhen Hong and Cheng Li and Richard C. Diamond and Da Yan and Qi Zhang and Xin Zhou and Siyue Guo and Kaiyu Sun and Jingyi Wang} }