01921nas a2200265   4500008004100000022001300041245007100054210006600125260001200191300001600203490000800219520115000227653002401377653000901401653001401410653002501424653004301449653002101492100001501513700001201528700001901540700001501559700001401574856006701588       2019                        eng d  a0306261900aA novel approach for selecting typical hot-year (THY) weather data0 anovel approach for selecting typical hotyear THY weather data  c03/2019  a1634 - 16480 v2423 a<p>The global climate change has resulted in not only warmer climate conditions but also more frequent extreme weather events, such as heat waves. However, the impact of heat waves on the indoor environment has been investigated in a limited manner. In this research, the indoor thermal environment is analyzed using a building performance simulation tool for a typical residential building in multiple cities in China, over a time period of 60 years using actual measured weather data, in order to gain a better understanding of the effect of heat wave events. The simulation results were used to analyze the indoor environment during hot summers. A new kind of weather data referred to as the typical hot year was defined and selected based on the simulated indoor environment during heat waves. The typical hot-year weather data can be used to simulate the indoor environment during extreme heat events and for the evaluation of effective technologies and strategies to mitigate against the impact of heat waves on the energy demand of buildings and human health. The limitations of the current study and future work are also discussed.</p>10aActual weather data10adest10aHeat wave10aMultiyear simulation10aResidential indoor thermal environment10aTypical hot year1 aGuo, Siyue1 aYan, Da1 aHong, Tianzhen1 aXiao, Chan1 aCui, Ying  uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930465902155nas a2200133   4500008004100000245009000041210006900131520167300200100001701873700001201890700001901902700001501921856008501936       2017                        eng d00aA Novel Stochastic Modeling Method to Simulate Cooling Loads in Residential Districts0 aNovel Stochastic Modeling Method to Simulate Cooling Loads in Re3 a<p>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.</p>1 aAn, Jingjing1 aYan, Da1 aHong, Tianzhen1 aSun, Kaiyu  uhttps://simulationresearch.lbl.gov/publications/novel-stochastic-modeling-method