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Explainable district heating load forecasting by means of a reservoir computing deep learning architecture

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  • Serra, Adrià
  • Ortiz, Alberto
  • Cortés, Pau Joan
  • Canals, Vincent

Abstract

The European Union (EU) stands at a critical juncture in its energy policy, particularly in the face of evolving global energy dynamics and the urgent need for climate action. This necessitates a paradigm shift towards a more efficient, interconnected, and digitally enhanced energy market, where the integration of renewable energy sources is prioritized. In this context, the role of load forecasting for district heating and cooling systems becomes increasingly significant, especially in the low temperature grids introduced with the 5th generation district heating system.

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  • Serra, Adrià & Ortiz, Alberto & Cortés, Pau Joan & Canals, Vincent, 2025. "Explainable district heating load forecasting by means of a reservoir computing deep learning architecture," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s036054422500283x
    DOI: 10.1016/j.energy.2025.134641
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    References listed on IDEAS

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    4. Liu, Zhikai & Dai, Ting & Zhang, Lian & Xu, Xin & Zhang, Qi & Wang, Yaran, 2025. "Hydrothermal modeling and decoupling analysis for secondary district heating systems: A digital twin approach," Energy, Elsevier, vol. 322(C).

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