Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (5): 146-156.doi: 10.3969/j.issn.1000-5641.2021.05.013

• Data Analysis and Applications • Previous Articles     Next Articles

Adaptive competitive equilibrium optimizer for power system customer classification

Sida ZHENG1(), Yan LIU1, Xiaokun YANG1, Chengfei QI1, Peisen YUAN2,*()   

  1. 1. Metrology Center, State Grid Jibei Electric Power Supply Company, Beijing 100045 China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2021-08-07 Online:2021-09-25 Published:2021-09-28
  • Contact: Peisen YUAN;


Accurate classification of power system customers can enable differentiated management and personalized services for customers. In order to address the challenges associated with accurate customer classification, this paper proposes a classification method based on an equilibrium optimizer and an extreme learning machine. In this method, an adaptive competition mechanism is proposed to balance the global exploration and local mining ability of an equilibrium optimizer, improving the performance of algorithms in finding optimal solutions. Thereafter, the proposed equilibrium optimizer is integrated with an extreme learning machine to classify the customers of a power system. Experiments on real data sets showed that the proposed algorithm integrated with an extreme learning machine offers more accurate performance for different classification indexes; hence, the proposed method can provide an effective technical means for power system customer management and service.

Key words: equilibrium optimizer, extreme learning machine, power system, customer classification

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