A Novel Method of Line Loss Rate Prediction for Substation Areas Based on Maximum Information Coefficient and BO-LSTNet

CUI Xianyi; DENG Hanjun; YU Minqi; XU Gang; REN Jiahao

doi:10.3969/j.issn.1008-0198.2025.05.012

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A Novel Method of Line Loss Rate Prediction for Substation Areas Based on Maximum Information Coefficient and BO-LSTNet

更新时间：2025-12-08

- A Novel Method of Line Loss Rate Prediction for Substation Areas Based on Maximum Information Coefficient and BO-LSTNet
- Vol. 45, Issue 5, Pages: 85-94(2025)
- 作者机构：
  
  1. 国网湖南省电力有限公司供电服务中心,湖南,长沙,410004
  2. 华北电力大学,北京,100096
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.05.012
  CLC： M714
- Published Online：11 November 2025，
  
  Published：11 November 2025
- 稿件说明：
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崔先迤, 邓汉钧, 余敏琪, 许刚, 任嘉浩. 基于最大信息系数与BO-LSTNet的新型台区线损率预测方法[J]. 湖南电力, 2025, 45(5): 85-94.

CUI Xianyi, DENG Hanjun, YU Minqi, et al. A Novel Method of Line Loss Rate Prediction for Substation Areas Based on Maximum Information Coefficient and BO-LSTNet[J]. 2025, 45(5): 85-94.
崔先迤, 邓汉钧, 余敏琪, 许刚, 任嘉浩. 基于最大信息系数与BO-LSTNet的新型台区线损率预测方法[J]. 湖南电力, 2025, 45(5): 85-94. DOI： 10.3969/j.issn.1008-0198.2025.05.012.

CUI Xianyi, DENG Hanjun, YU Minqi, et al. A Novel Method of Line Loss Rate Prediction for Substation Areas Based on Maximum Information Coefficient and BO-LSTNet[J]. 2025, 45(5): 85-94. DOI： 10.3969/j.issn.1008-0198.2025.05.012.

摘要

针对传统台区线损预测方法存在无法灵活调节、不具备实时性、精度受气象条件影响等缺陷

提出一种基于最大信息系数与BO-LSTNet的新型台区的线损率预测方法。利用最大信息系数方法筛选台区气象信息

对输入变量筛选、清洗

最后将线损数据放入经过贝叶斯优化后的LSTNet模型中。仿真实验结果表明

相较过去的预测手段

该模型对新型台区线损率预测的适应性更高

解决了实际工程中线损预测精度较低的问题。

Abstract

Aiming at the shortcomings of traditional methods for predicting line loss in substation areas

such as inability to adjust flexibly

lack of real-time performance

and accuracy affected by meteorological conditions

a new method for predicting line loss rate in substation areas based on maximum information coefficient and BO-LSTNet is proposed. The maximum information coefficient method is used to screen meteorological information in the substation area

and input variables are filtered and cleaned

and finally the line loss data is put into the LSTNet model after Bayesian optimization. The reliability is verified through simulation experiments

and the results show that compared with previous prediction methods

this model has higher adaptability to the new type of substation line loss rate prediction

and solves the problem of low accuracy in line loss prediction in practical engineering.

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