基于对抗训练的多因素短期电力负荷预测

doi:10.3969/j.issn.1008-0198.2025.04.020

湖南电力 ›› 2025, Vol. 45 ›› Issue (4): 143-150.doi: 10.3969/j.issn.1008-0198.2025.04.020

• 电力人工智能与数字化 • 上一篇

基于对抗训练的多因素短期电力负荷预测

李晓萍¹, 何璐兵¹, 尚龙康²

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

收稿日期:2025-05-16 修回日期:2025-06-12 出版日期:2025-08-25 发布日期:2025-09-05
通信作者: 李晓萍（1997）,女,助教,研究方向是新能源电力系统。
作者简介:何璐兵（1988）,女,讲师,主要研究方向为电气自动化。尚龙康（1997）,男,硕士研究生,主要研究方向为电气自动化。

Multi-Factor Short-Term Power Load Forecasting Based on Adversarial Training

LI Xiaoping¹, HE Lubing¹, SHANG Longkang²

1. College of Electrical Engineering, Xuchang Electrical Vocational College, Xuchang 461000, China;
2. Xuchang Xuji Software Technology Co., Ltd., Xuchang 461000, China

Received:2025-05-16 Revised:2025-06-12 Online:2025-08-25 Published:2025-09-05

摘要/Abstract

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。

关键词: 电力负荷预测, 深度学习, 对抗样本, 多因素分析, 对抗训练

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.

Key words: power load forecasting, deep learning, adversarial sample, multi-factor analysis, adversarial training

中图分类号:

TM715.1

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]. Hunan Electric Power, 2025, 45(4): 143-150.

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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

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