基于运行大数据的长江流域家用多联机系统部分负荷运行特性分析
Analysis of partial load operation characteristics for household multi-split system in the Yangtze River basin based on operation big data
摘要：
长江流域家用空调的运行特性一直是学者们关注的焦点,然而前期的研究一般以分体式的挂机或柜机为研究对象,针对家用多联机的部分负荷特性的研究相对缺乏。基于长江流域家用多联机的运行大数据,分别从冬夏两季、全时段、不同时段、不同城市多方面对该地区3个典型城市的部分负荷运行特性进行了量化分析;然后分析了对多联机系统功率的影响因素,并基于多种影响因素与机组运行功率的耦合关系,开发了系统功率的预测模型,基于该模型对家用多联机系统的节能潜力进行了讨论。研究结果表明:对于家用多联机系统,其夏季部分负荷率在30%～60%之间的占比最大,达到48.4%,其冬季部分负荷率小于30%的占比最大,达到59.7%;家用多联机系统在不同季节、不同城市、不同时段下的部分负荷特性均存在差异;与此同时,系统负荷需求和系统部分负荷率对系统功率的影响最大,基于本研究开发的系统功率预测模型,当负荷需求较低时,将18、16、14 kW的机组容量分别调整至16、14、12 kW后,夏季制冷模式功率下降了7%～13%,冬季制热模式功率下降了3%～22%,因此,对于在一定环境工况下低负荷运行时间占比较大的用户,适当匹配稍小的机组容量(提升部分负荷率),将有助于系统节能。该研究结论可为家用多联机的性能开发、运行控制逻辑优化、设备选型提供数据支撑与参考。
Abstract：
The operation characteristics of household air conditioners in the Yangtze River basin have always been the focus of scholars’ attention. However, the previous research generally focuses on split-type wall-mounted or cabinet-type units, and the research on the partial load characteristics of household multi-split units is relatively lacking. Based on the operation big data of household multi-split units in the Yangtze River basin, the partial load operation characteristics of three typical cities in the Yangtze River basin are quantitatively analysed from the aspects of winter and summer, full-time, different periods, and different cities. Then, the influencing factors of the power of the multi-split system are analysed, and based on the coupling relationship between various influencing factors and the operation power of the units, a prediction model of the system power is developed, and the energy-saving potential of the household multi-split system is discussed based on this model. The results show that for the household multi-split system, the partial load rate between 30% to 60% accounts for the largest proportion in summer, reaching 48.4%, and the partial load rate of less than 30% accounts for the most in winter, reaching 59.7%. There are differences in the partial load characteristics of household multi-split systems in different seasons, different cities, and different periods. At the same time, the system load demand and the partial load rate of the system have the greatest impact on the system power. Based on the system power prediction model developed in this study, when the load demand is lower, the power of the 18, 16, and 14 kW units is adjusted to 16, 14, and 12 kW, respectively, the power of the cooling mode decreases by 7% to 13% in summer, and the power in the heating mode decreases by 3% to 22% in winter. Therefore, for the users who account for a large proportion of low-load operation time under certain environmental conditions, appropriately matching the slightly smaller unit capacity (increasing the partial load rate) will help the system save energy. The study conclusion can provide data support and reference for the performance development, operation control logic optimization, and equipment selection of household multi-split units.
Keywords:the Yangtze River basin; household; multi-split system; partial load; operation big data