| Summary: | Due to the increase in the number of smart meter devices, a power grid generates a large amount of data. Analyzing the data can help in understanding the users’ electricity consumption behavior and demands; thus, enabling better service to be provided to them. Performing power load profile clustering is the basis for mining the users’ electricity consumption behavior. By examining the complexity, randomness, and uncertainty of the users’ electricity consumption behavior, this paper proposes an ensemble clustering method to analyze this behavior. First, principle component analysis (PCA) is used to reduce the dimensions of the data. Subsequently, the single clustering method is used, and the majority is selected for integrated clustering. As a result, the users’ electricity consumption behavior is classified into different modes, and their characteristics are analyzed in detail. This paper examines the electricity power data of 19 real users in China for simulation purposes. This manuscript provides a thorough analysis along with suggestions for the users’ weekly electricity consumption behavior. The results verify the effectiveness of the proposed method. Keywords: Users’ electricity consumption, Ensemble clustering, Dimensionality reduction, Cluster validity
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