02155nas 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