现代电力2026，Vol.43Issue(3)：422-432,11.DOI:10.19725/j.cnki.1007-2322.2024.0072
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
摘要
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
负荷预测/迁移学习/源域选择/时间卷积网络/雪消融优化器Key words
load forecasting/transfer learning/source domain selection/temporal convolutional network(TCN)/snow ablation optimizer分类
信息技术与安全科学引用本文复制引用
罗同桐,唐志远,唐義坤,刘俊勇..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):422-432,11.基金项目
四川省科技计划项目(2023YFSY0033).Science and Technology Planning Project of Sichuan Province(2023YFSY0033). （2023YFSY0033）
