Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (6): 846-854.doi: 10.23940/ijpe.20.06.p3.846854

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Prediction of Electricity Tariff Recovery Risk based on Hybrid Feature Selection Algorithm

Shenyi Qian, Yongsheng Shi*, Huaiguang Wu, and Songtao Shang   

  1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:
  • About author:Shenyi Qian received the B.S. degree from Huazhong University of Science and Technology. He is currently an associate professor of School of Computer and Communication Engineering at Zhengzhou University of Light Industry. His research interests include data mining, business intelligence, computer software and theory.
    Yongsheng Shi received the bachelor’s degree from Zhengzhou University of Light Industry, China, in 2017. He will receive master’s degree from School of computer and Communication Engineering at Zhengzhou University of Light Industry, China, in 2020. His main research interests in computer technology, artificial intelligence and machine learning.
    Huaiguang Wu was born in Shandong province, CHINA in 1976. He received PhD degrees in computing from Wuhan University in 2011. He has been in the School of Computer and Communication Engineering at Zhengzhou University of Light Industry. he is a postdoctoral fellow at Peking University and a research visitor of in the university of Edinburgh from 2017 to 2018. His research interests include formal methods, software engineering and algorithms.
    Songtao Shang Zhengzhou University of Light Industry, lecturer, received the Ph.D. degree from Communication University of China. His research interests include Big Data, machine learning, and data mining.
  • Supported by:
    This research is financially supported by National Natural Science Foundation of China (No. 61672470), and the Science and Technology Project of Henan Province (No. 202102210351), and the Doctoral Program of Zhengzhou University of Light Industry (No. 2017BSJJ046), and the Key Research Projects of Henan Higher Education Institutions (No. 20A120011).

Abstract: In order to fully extract the information that affects the user's arrears and reduce the dimension of features, a hybrid feature selection algorithm based on the particle swarm optimization algorithm with contraction factor (PSOCF) and whale optimization algorithm (WOA), namely, PSOCFWOA is proposed. The PSOCFWOA algorithm combines the advantages of the two algorithms that PSOCF and WOA. The experimental results show that the proposed PSOCFWOA can effectively reduce a large number of redundant or irrelevant features and stably improve the prediction results in the case of low execution time, compared with two state-of-the-art optimization algorithm, and six well-known feature selection approaches to the risk prediction of electricity tariff recovery for power customers.

Key words: electricity tariff recovery, particle swarm optimization algorithm, whale optimization algorithm, feature selection