近年来，区域供暖和制冷（DHC）系统在中国和世界各地越来越流行，因为它们具有较高的设备效率和使用社区规模可再生能源的能力。从能源使用和城市景观的角度来看，区域供冷系统具有巨大的潜在价值。然而，在中国，大多数区域冷却系统并没有达到最佳效率。根据一些测量结果，大量未以最佳效率运行的现有系统尺寸过大。 过大的系统会导致投资浪费、运营效率低下，从而造成能源浪费。为此，需要准确了解某一地区建筑用户的热负荷需求。与一栋房屋或一栋住宅楼不同，一个地区有几十户或数百户不同的热需求。不同家庭的空间和时间多样性导致了不同家庭的中央供应负荷的不同特征。为了分析不同规模系统（如家用空调、中央DHC系统服务数十套公寓、中央DHC系统服务数百套公寓）之间的特征差异，我们收集了中国郑州一个住宅区智能电表的家庭小时负荷，进行定量分析，它有一个中央地源热泵系统，为三高温室提供制冷和供暖- 拔地而起。然后，根据峰值负荷、总热耗和负荷分布的变化以及典型日负荷波动的实际数据，对负荷进行了比较，分析了不同户数组合下的热负荷差异。最后，我们还讨论了DHC系统中的负载特性对地区暖通空调系统规模的影响。引用:2018年年度会议，德克萨斯州休斯顿，会议论文

District heating and cooling (DHC) systems have become increasingly popular in recent years in China and all around the world, due to their high equipment efficiency and the capability of using community-scale renewable energy sources. From the perspective of energy use and urban landscape, district cooling systems have great potential value. However, in China, most of the district cooling systems have not performed at their optimal efficiencies. According to some measurement results, a large amount of existing systems that are not performing at optimal efficiencies are oversized. Oversized systems lead to wasted investment, low operational efficiency and, thus, waste of energy. To this end, the thermal load demands of building users in a district need to be accurately understood. In contrast to a single house or a residential building, a district has tens or hundreds of households with varying thermal demands. Spatial and temporal diversity of various households results in different features of central supply load from individual household. In order to analyze the characteristic differences among systems with different scales (e.g., household air conditioner, central DHC system serving tens of apartments, central DHC system serving hundreds of apartments), we collected the household hourly loads from the smart meters in one residential district in Zhengzhou, China for quantitative analyses, which has a central ground source heat pump system to supply the cooling and heating for the three high-rise buildings. Then, we compared the load analyzed the thermal load diversity among combinations of different number of households based on real data from the variation of peak loads, total thermal consumptions and load distributions, as well as the load volatility in typical day. Finally, we also discussed the impact of load characteristics in DHC systems on district HVAC system sizing.