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.

Key words:  large office building, cluster ensemble, comprehensive clustering evaluation index, consumption pattern, energy conservation