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

Surya Venkatesh Pandiyan, Sebastien Gros and Jayaprakash Rajasekharan

Applied Energy, 2025, vol. 380, issue C, No S0306261924024218

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.

Keywords: Physics-informed neural network; Electric water heater; Temperature prediction; Smart energy management; Demand response (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.125037

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