建筑能源需求侧管理的数据挖掘方法及案例分析
Data mining method and case analysis of building energy demand side management
摘要：
简要介绍了4种常用的数据挖掘技术，结合建筑负荷分类和预测相关研究进行了评估比较。使用K均值聚类算法对上海某居民小区的用电数据进行了分析，结果表明，利用K均值算法的聚类分析和统计学方法结合所挖掘出的数据与实际情况表现出高度吻合性，并且比逻辑推理分析的结果更加精确可靠。
关键词：人行为,需求侧管理,数据挖掘,K均值聚类算法,节能,建筑能耗
Abstract：
Briefly presents four common data mining techniques and evaluates them based on related studies of building load classification and prediction. Analyses the electricity data of a residential community in Shanghai by means of K mean clustering algorithm, and the result shows a high degree of consistency between the clustering analysis and statistical method using the K mean algorithm combining with the data mined and the actual situation, and more accurate and reliable than the results of logical reasoning.
Keywords:occupant behavior, demand side management, data mining, K mean algorithm, energy saving, building energy consumption