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Physics informed neural network based multi-zone electric water heater modeling for demand response

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  • Pandiyan, Surya Venkatesh
  • Gros, Sebastien
  • Rajasekharan, Jayaprakash

Abstract

Devising an effective control strategy to maximize the flexibility potential of electric water heaters (EWHs) requires a highly accurate and computationally inexpensive EWH model. Existing physics-based models are either too simplistic or computationally complex. This paper models EWHs using a physics-informed neural network (PINN) that integrates domain knowledge into the training process to ensure better physical consistency for capturing EWH thermal dynamics at a lower computational cost. Using a physics-based multi-zone (MZ) differential equation model (DEM), the EWH is discretized into multiple zones and modeled using a standard Multiple-Input-Multiple-Output (MIMO) PINN architecture to develop a generic and efficient EWH model. To improve the accuracy and interpretability further, a hybrid model that employs a Multiple-Input-Single-Output (MISO) PINN architecture together with physics derived features from the MZ DEM and a custom designed function for resolving temperature inversion is investigated in detail. Additionally, a customized recursive training strategy is developed to enable longer time-horizon simulations without performance degradation. Performance evaluations in both simulation and optimization frameworks using real-world data demonstrate the computational gains offered by PINN models over traditional MZ DEM.

Suggested Citation

  • Pandiyan, Surya Venkatesh & Gros, Sebastien & Rajasekharan, Jayaprakash, 2025. "Physics informed neural network based multi-zone electric water heater modeling for demand response," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924024218
    DOI: 10.1016/j.apenergy.2024.125037
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    References listed on IDEAS

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    1. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
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    3. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    4. Pied, Marie & Anjos, Miguel F. & Malhamé, Roland P., 2020. "A flexibility product for electric water heater aggregators on electricity markets," Applied Energy, Elsevier, vol. 280(C).
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