Data-driven model for heat load prediction in buildings connected to district heating networks

Alaeddine Hajri, Roberto Garay-Marinez, Ana M. Macarulla and Mohamed Amin Ben Sassi

Energy, 2025, vol. 329, issue C

Abstract: Energy systems such as District Heating Systems are shifting towards efficiency and use of renewable energy. The optimal operation of these systems requires of substantial improvement in load prediction processes, suitable to map the peformance to weather conditions, as well as usage patterns. Several existing works have created a background of knowledge where Time of Week (ToW) segmentation, Multiple Linear Regression (MLR) and Auto Regression (AR) present promising results. But by doing so, these models are either too complex, or lack interpretability.

Keywords: Data-driven model; Heat loads; Short-term forecasting; Energy in buildings (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:329:y:2025:i:c:s0360544225023266

DOI: 10.1016/j.energy.2025.136684

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