Abstract：The installation of smart meters has resulted in the accumulation of massive electricity data at the demand side. Feature extraction and clustering analysis, as effective data processing means, can help utilities to mine the valuable information hidden in the data and extract customer behavior characteristics. In order to extract effective and intuitive load characteristics, this paper proposes a clustering algorithm based on affinity propagation (AP) clustering algorithm with optimized symbolic aggregate approximation (SAX) and weighted load characteristic indices. First, to solve the problem of high complexity, SAX algorithm is applied to reduce the dimension of load curves, and the appropriate symbolic representation scheme is obtained by simulated annealing particle swarm optimization. Then, combined with the load characteristic indices, the improved AP clustering algorithm is utilized to cluster load curves. In addition, to avoid the subjectivity of the index setting, entropy weighting method is used to objectively weight the load characteristic indices. Based on the clustering results, the consumption behavior and demand response potential of different customers are analyzed. Case studies indicate that the proposed methods and approaches is efficient and effective, which can be applied in utilities for decision making, such as load forecasting, anomaly detection, differential services, etc.
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2019, Vol. 34 
