【摘要】 以上海市长宁区的大型办公建筑为研究对象,利用数据分析方法分析其用电行为与节能潜力。针对传统用电行为分析,采用单一聚类算法拓展性较差的问题,文章提出通过优选方法进行聚类融合以吸收不同算法优点,增强算法适应能力。首先进行方法优选,针对聚类效果评价指标的不一致问题,提出综合聚类评价指标并对R语言库中大量的单一聚类方法进行评价,采用基于簇的相似度划分算法(CSPA)进行聚类融合。试验集的结果表明该聚类融合方法具有更好的有效性。利用该改进聚类融合算法对用户负荷曲线进行聚类,提取用户用电模式,分析其用电构成与特征,并进行节能策略的分析。结果表明,该办公类建筑具有4类基本用电模式,且有一定节能潜力。更多还原

【Abstract】 In this paper,a large office building in Changning District( Shanghai) is studied to analyze its electricity consumption behavior and energy-saving potential using data analysis methods. A cluster ensemble model using optimizing clustering algorithms is proposed to solve the problem of poor scalability of single clustering algorithms,which are used frequently in this field. Firstly,during the period of selecting algorithms,a comprehensive clustering evaluation index is proposed for the problem of the inconsistency of indicators. Then different clustering algorithms in R library are evaluated,and results are fused by cluster-based similarity partitioning algorithm( CSPA). The results show that the cluster ensemble model is more effective. Users’ consumption patterns are extracted by this improved cluster ensemble algorithm. Then constitution and characteristics of different patterns and energy conservation strategies are analyzed. The results show that there are 4 different consumption patterns and certain energy saving potential of this large-scale office building.更多还原