基于K-means聚类的热计量用户负荷特性及行为节能分析

K-means Clustering Based Analysis of Load Characteristics and Behavioral Energy Saving of Heat Metering Users

采暖建筑进行热计量改造后,用户可以根据热需求进行室内温度控制和调节,由于用户设定室内温度不同,从而形成不同的热负荷特性。根据热计量用户用热负荷作为分析依据,通过采集热用户室内温度、设定温度、耗热量、开启及断开时间等数据,采用K-means算法对数据进行聚类,以此获取热计量用户负荷特性。利用聚类结果及居住位置对热用户的行为节能进行分析,得出不同居住位置用户室温、设定温度以及热负荷规律,为用户按需用热、热力公司按需供热提供参考,为合理地开展采暖建筑节能起到促进作用。

After the heating building is retrofitted by heat metering,the user can perform indoor temperature control and adjustment according to the heat demand. Because the user sets the indoor temperature differently,different heat load characteristics are formed. According to the heat metering user’s thermal load as the analysis basis,through the collection of hot user indoor temperature,set temperature,heat consumption,turn on and off time and other data,using K-means algorithm to cluster the data,in order to obtain heat metering User load characteristics. The clustering results and living positions are applied to analyze the behavioral energy saving of hot users,the user’s room temperature,set temperature,and heat load law of different residential locations are obtained,which provides reference for users on-demand heating and heating company’s on-demand heating,and is reasonable to promote heating energy efficiency in buildings to play a catalytic role.