智能系统学报2025，Vol.20Issue(6)：1379-1391,13.DOI:10.11992/tis.202501003
多尺度路由时空注意力的综合能源多元负荷预测
Integrated energy multiple load forecasting for multiscale routing spatiotemporal attention
摘要
Abstract
Accurate multi-energy load forecasting is critical for the stable operation of integrated energy systems(IES).Existing methods often fail to capture the complex interactions among electricity,cooling,and heating loads,thereby limiting forecasting effectiveness.To address this challenge,this study first conducted an in-depth analysis of the statist-ical features of multi-energy loads,their seasonal-intraday coupling patterns,and their correlations with weather factors.Based on these insights,a multiscale spatiotemporal routing attention model was proposed for multi-energy load fore-casting in IES.The model incorporates multikernel local decomposition to extract multiscale periodic and trend features,while a routing spatiotemporal attention mechanism,coupled with a multiscale encoder-decoder,is designed to capture inter-load dependencies and load-weather correlations.Periodic forecasts produced by this framework are further com-bined with trend predictions from recurrent neural networks to generate the final outcomes.Extensive evaluations on real-world datasets,including coupling analysis,ablation studies,and comparative experiments,demonstrate that the proposed model consistently outperforms mainstream methods such as LSTM,Transformer,CNN-GRU,Autoformer,and FEDformer,across varying levels of load coupling strength.关键词
综合能源/多元负荷预测/多尺度/多核局域分解/路由时空注意力/周期性/趋势性/耦合性/相关性Key words
integrated energy/multiple load forecasting/multiscale/multicore local decomposition/routing spatiotem-poral attention/periodicity/tendency/coupling/correlation分类
信息技术与安全科学引用本文复制引用
WANG Dewen,ZHANG Linfei,MIAO Qingjian,LI Chenghao,ZHAO Wenqing..多尺度路由时空注意力的综合能源多元负荷预测[J].智能系统学报,2025,20(6):1379-1391,13.基金项目
国家自然科学基金项目(62371188). （62371188）
