[1]WANG Dewen,ZHANG Linfei,MIAO Qingjian,et al.Integrated energy multiple load forecasting for multiscale routing spatiotemporal attention[J].CAAI Transactions on Intelligent Systems,2025,20(6):1379-1391.[doi:10.11992/tis.202501003]
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Integrated energy multiple load forecasting for multiscale routing spatiotemporal attention

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 6 Page number: 1379-1391 Column: 学术论文—机器学习 Public date: 2025-11-05
Title:
Integrated energy multiple load forecasting for multiscale routing spatiotemporal attention
Author(s):
WANG Dewen1; 2;  ZHANG Linfei1;  MIAO Qingjian1;  LI Chenghao1;  ZHAO Wenqing1; 3
1. Department of Computer, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Knowledge Computing for Energy & Power, Baoding 071003, China;
3. Engineering Research Center of Intelligent Computing for Complex En
Keywords:
integrated energy; multiple load forecasting; multiscale; multicore local decomposition; routing spatiotemporal attention; periodicity; tendency; coupling; correlation
CLC:
TP399; TM721
DOI:
10.11992/tis.202501003
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 statistical 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 forecasting 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 combined 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.
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