电力系统及其自动化学报2025，Vol.37Issue(4)：88-97,10.DOI:10.19635/j.cnki.csu-epsa.001483
跨建筑短期负荷预测的深度迁移学习方法
Deep Transfer Learning Method for Short-term Load Forecasting Across Buildings
摘要
Abstract
To address the issue of limited accuracy in deep learning prediction models due to insufficient data,a deep transfer learning method that integrates the cross-attention in Transformer(CATrans)and domain separation networks(DSN)is proposed,i.e.,CATrans-DSN,which can be used for short-term load forecasting cross buildings.The CATrans feature extractor utilizes the attention mechanism to learn domain-common and domain-specific temporal fea-tures from load data in both the source and target domains,and it also uses the common features for knowledge transfer.The feature reconstructor serves as an auxiliary module to reconstruct data for both the source and target domains.After-wards,a regression predictor translates the learned features into forecasted values.Finally,the building load fore-casting models trained on both the source and target domains are directly applied to the target building for load prediction.Experimental results demonstrate that the proposed method effectively improves the predictive accuracy and the model's generalization capability under conditions of data scarcity.关键词
负荷预测/交叉注意力机制/重构域适应/迁移学习Key words
load forecasting/cross-attention mechanism/reconstructive domain adaptation/transfer learning分类
计算机与自动化引用本文复制引用
闫秀英,门琪,吴晓雪..跨建筑短期负荷预测的深度迁移学习方法[J].电力系统及其自动化学报,2025,37(4):88-97,10.基金项目
陕西省自然科学基金资助项目(Z20220068). （Z20220068）
