Multi-scale temporal representation with sparse dynamic graph learning for district heat load forecasting

Yaohui Huang, Peisong Zhang, Zhenkun Lu and Zhikai Ni

Energy, 2025, vol. 333, issue C

Abstract: The diverse temporal patterns of heat demand in district heating systems (DHS), pose significant challenges to achieving accurate forecasting. Graph neural networks (GNNs) exhibit promise in capturing these spatio-temporal dependencies, but existing models are limited by focusing on single-time-scale temporal patterns. While multi-scale graph representations in GNNs could introduce more timing-dependent features, its high computational cost constrains wider real-world application. Addressing this, we propose a Multi-scale Sparse Dynamic Graph Neural Network (MSDGN) for district heat load forecasting. MSDGN uses a multi-scale dynamic graph structure to learn various temporal dependencies without pre-defined priors. It includes a temporal attention mechanism, which reduces computational costs by sparsifying the graph’s edges. Additionally, MSDGN also integrates a spatio-temporal enhancement module and a residual fusion module, efficiently extracting features across scales and including recent short-term trends. Comprehensive experiments with real-world data from 3021 heat meters demonstrate MSDGN’s effectiveness and superiority over other state-of-the-art methods across various settings.

Keywords: District heating; Load forecasting; Dynamic graph neural network; Representation learning; Multi-scale features (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225024806

DOI: 10.1016/j.energy.2025.136838

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