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Abnormal detection of building energy consumption is very important for building management and operation. In this paper, a multi-partition (MP) clustering algorithm for imbalanced data based on D-S evidence theory is proposed, and the energy consumption anomaly detection model of MP algorithm is constructed to accurately detect the abnormal values in building energy consumption. Firstly, the energy consumption data set is divided into multiple parts by the improved credal c-means algorithm. The KNN-based Mean-shift algorithm is used to determine the number of real categories of the data set. Then the energy consumption data is merged according to the density merging rules. Finally, the energy consumption data that is not merged is divided again to get the final abnormal energy consumption detection results. The UCI data set verification results show that the MP algorithm has a good clustering effect for imbalanced data, which can effectively avoid the "uniform effect" of samples and reduce the error rate. Through detecting the abnormal values of the energy consumption of air conditioning and lighting in a large shopping mall, the validity of the energy consumption anomaly detection model of MP algorithm is verified.