基于时间卷积网络与迁移学习的短期负荷预测

罗同桐; 唐志远; 唐義坤; 刘俊勇

doi:10.19725/j.cnki.1007-2322.2024.0072

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基于时间卷积网络与迁移学习的短期负荷预测

智能电网 | 更新时间：2026-06-02

- 基于时间卷积网络与迁移学习的短期负荷预测
- Short-term Load Forecasting Based on Temporal Convolutional Network and Transfer Learning
- 现代电力 2026年43卷第3期页码：422-432
- 作者机构：
  
  四川大学电气工程学院,四川省,成都市,610065
- 作者简介：
  
  罗同桐(2000)，女，硕士研究生，研究方向为人工智能和负荷预测，E-mail：luotongtong@stu.scu.edu.cn
  唐志远(1992)，男，博士，副研究员，通信作者，研究方向为电力系统控制及规划运行、分布式控制及优化，E-mail：tangzhiyuan@scu.edu.cn
  唐義坤(1997)，男，硕士研究生，研究方向为机器学习和负荷预测，E-mail：tangyikun@stu.scu.edu.cn
  刘俊勇(1963)，男，博士，教授，研究方向为电力市场、智能配电网、电力系统大数据技术、电动汽车，E-mail：liujy@scu.edu.cn
- 基金信息：
  
  四川省科技计划项目（2023YFSY0033）。
- DOI：10.19725/j.cnki.1007-2322.2024.0072
  中图分类号： 3
- 纸质出版：2026
- 稿件说明：
移动端阅览
罗同桐, 唐志远, 唐義坤, 等. 基于时间卷积网络与迁移学习的短期负荷预测[J]. 现代电力, 2026,43(3):422-432.

LUO Tongtong, TANG Zhiyuan, TANG Yikun, et al. Short-term Load Forecasting Based on Temporal Convolutional Network and Transfer Learning[J]. 2026, 43(3): 422-432.
罗同桐, 唐志远, 唐義坤, 等. 基于时间卷积网络与迁移学习的短期负荷预测[J]. 现代电力, 2026,43(3):422-432. DOI： 10.19725/j.cnki.1007-2322.2024.0072.

LUO Tongtong, TANG Zhiyuan, TANG Yikun, et al. Short-term Load Forecasting Based on Temporal Convolutional Network and Transfer Learning[J]. 2026, 43(3): 422-432. DOI： 10.19725/j.cnki.1007-2322.2024.0072.

摘要

短期负荷预测对于优化能源供需平衡、提高电力系统运行效率至关重要。部分地区由于缺乏相关负荷历史数据集，难以使用传统的机器学习预测方法。针对数据稀缺情况下的短期负荷预测问题，提出一种基于时间卷积网络（temporal convolutional network

TCN）与迁移学习的混合预测模型。首先，为了准确度量源域的可迁移性，提出一种综合考虑数据间距离与特征分布差异的源域选择方法。该方法通过结合马氏距离与最大均值差异，筛选出与目标域具有高度相似性的源域。其次，利用雪消融优化器（snow ablation optimizer

SAO）对TCN模型的超参数进行优化，建立SAO-TCN迁移模型，以提高整体的预测效果。最后，使用实际数据集对所提混合模型进行性能评估和有效性验证。实验结果表明，相较于传统机器学习方法，所提混合模型能够增加源域选择的范围与准确性，它将均方根误差至少降低15%。

Abstract

Short-term load forecasting is crucial for optimizing energy supply and demand balance

which improves the efficiency of power system operation. In regions where historical load data are scarce

traditional machine learning prediction methods face great challenges. To address the short-term load forecasting issue in the context of sparse data

we propose a hybrid prediction model based on temporal convolutional network (TCN) and transfer learning. Firstly

a source domain selection method combining Mahalanobis distance and maximum mean discrepancy is introduced to measure the transferability of the source domain. This method aims to identify a source domain that is highly similar to the target domain in terms of data distribution and feature differences. Secondly

the snow ablation optimizer (SAO) is utilized to optimize the hyperparameters of the TCN model

thereby establishing an SAO-TCN transfer model to enhance overall prediction performance. Finally

the proposed hybrid model is evaluated and validated using real-world datasets. Experimental results demonstrate that in comparison to traditional machine learning methods

the hybrid model is capable of expanding the range of source domain selection and enhancing its accuracy as well

and it reduces the root mean square error by at least 15%.

关键词

Keywords

references

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