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Dynamic real-time optimisation of district heating networks with online time-series training and machine learning forecasts

Author

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  • Tijani, Olamilekan E.
  • Untrau, Alix
  • Reneaume, Jean-Michel
  • Viot, Hugo
  • Serra, Sylvain

Abstract

This study investigates the application of Dynamic Real-Time Optimisation (DRTO) to enhance the operational efficiency and decarbonisation of district heating networks (DHNs). Two optimisation strategies were compared: a standalone offline dynamic optimisation (planning) approach and a DRTO framework capable of online learning and re-optimisation. Since disturbance variables such as power demand and soil temperature cannot be known in advance, four machine learning (ML) models were employed to forecast them: Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and eXtreme Gradient Boosting (XGBoost) model. Using 2018 ASHRAE and Open-Meteo data, XGBoost achieved the highest prediction accuracy, outperforming all neural-network models.

Suggested Citation

  • Tijani, Olamilekan E. & Untrau, Alix & Reneaume, Jean-Michel & Viot, Hugo & Serra, Sylvain, 2026. "Dynamic real-time optimisation of district heating networks with online time-series training and machine learning forecasts," Energy, Elsevier, vol. 348(C).
    • Handle: RePEc:eee:energy:v:348:y:2026:i:c:s0360544226005475
    DOI: 10.1016/j.energy.2026.140444
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