基于对抗训练的多因素短期电力负荷预测

李晓萍; 何璐兵; 尚龙康

doi:10.3969/j.issn.1008-0198.2025.04.020

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基于对抗训练的多因素短期电力负荷预测

电力人工智能与数字化 | 更新时间：2025-12-08

- 基于对抗训练的多因素短期电力负荷预测
- Multi-Factor Short-Term Power Load Forecasting Based on Adversarial Training
- 湖南电力 2025年45卷第4期页码：143-150
- 作者机构：
  
  1. 许昌电气职业学院电气工程学院,河南,许昌,461000
  2. 许昌许继软件技术有限公司,河南,许昌,461000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.04.020
  中图分类号： TM715.1
- 网络出版：2025-09-05，
  
  纸质出版：2025-09-05
- 稿件说明：
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李晓萍, 何璐兵, 尚龙康. 基于对抗训练的多因素短期电力负荷预测[J]. 湖南电力, 2025, 45(4): 143-150.

LI Xiaoping, HE Lubing, SHANG Longkang. Multi-Factor Short-Term Power Load Forecasting Based on Adversarial Training[J]. 2025, 45(4): 143-150.
李晓萍, 何璐兵, 尚龙康. 基于对抗训练的多因素短期电力负荷预测[J]. 湖南电力, 2025, 45(4): 143-150. DOI： 10.3969/j.issn.1008-0198.2025.04.020.

LI Xiaoping, HE Lubing, SHANG Longkang. Multi-Factor Short-Term Power Load Forecasting Based on Adversarial Training[J]. 2025, 45(4): 143-150. DOI： 10.3969/j.issn.1008-0198.2025.04.020.

摘要

为提升电力负荷预测的精度及稳定性

提出一种基于对抗训练的多因素电力负荷预测模型来进行短期电力负荷预测。该方法结合历史负荷数据及预测当天的天气等特征进行电力负荷预测

通过对预测模型进行对抗训练来提升模型对于对抗样本的鲁棒性。在公共数据集上的实验结果表明

该方法在预测精度上优于仅考虑历史负荷数据的同类方法

且对于对抗样本表现出更好的鲁棒性。

Abstract

In order to improve the accuracy and stability of power load forecasting

a multi-factor power load forecasting model based on adversarial training is proposed for short-term power load forecasting. This method combines historical load data and the weather and other characteristics of the forecast day to predict the power load and enhances the robustness of the prediction model to adversarial samples through adversarial training. Experimental results on a public dataset show that this method outperforms similar methods that only consider historical load data in terms of prediction accuracy and shows better robustness to adversarial samples.

关键词

Keywords

references

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