Credit Evaluation of Electricity Sales Companies Based on Improved Coefficient of Variation Method and BP Neural Network

被引：0

作者：

Li Y. ^{[1
]}

Li F. ^{[1
]}

Wang S. ^{[1
]}

Shang Q. ^{[1
]}

机构：

[1] School of Electrical Engineering, Xinjiang University, Xinjiang Uygur Autonomous Region, Urumqi

来源：

Dianwang Jishu/Power System Technology | 2022年 / 46卷 / 11期

基金：

中国国家自然科学基金;

关键词：

BP neural network; credit evaluation; electricity market; electricity sales company; Harris hawk optimization algorithm; improved coefficient of variation method;

D O I：

10.13335/j.1000-3673.pst.2022.0062

中图分类号：

学科分类号：

摘要：

Aiming at the characteristics of electricity sales companies in the electricity market, an evaluation model based on the improved coefficient of variation method and the improved Harris Hawk-Back Propagation neural network is proposed. First, a characteristic credit evaluation index system including the renewable energy consumption (include cross-provincial delivery), the service quality and the carbon emission management is constructed, and the objective weight of each evaluation index is given by using the improved coefficient of variation method (ICVM). Second, the improved Harris Hawk Optimization algorithm (THHO) combined with the adaptive t-distribution mutation operator is introduced into the BP neural network algorithm to optimize its weights and thresholds, thus the problems that the BP neural network algorithm has a slow convergence speed and is easy to fall into a local optimum are improved to realize the credit evaluation of the retail company. Finally, a comparative analysis with various models in terms of the evaluation result, the evaluation accuracy, the convergence rate and the optimal value error, etc., verifies the feasibility and excellence of the proposed model. © 2022 Power System Technology Press. All rights reserved.

引用

页码：4228 / 4237

页数：9

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