Improving Thermochemical Energy Storage Dynamics Forecast with Physics-Inspired Neural Network Architecture

Timothy Praditia, Thilo Walser, Sergey Oladyshkin and Wolfgang Nowak
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Timothy Praditia: Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Universität Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany
Thilo Walser: Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Universität Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany
Sergey Oladyshkin: Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Universität Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany
Wolfgang Nowak: Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems, Universität Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart, Germany

Energies, 2020, vol. 13, issue 15, 1-26

Abstract: Thermochemical Energy Storage (TCES), specifically the calcium oxide (CaO)/calcium hydroxide (Ca(OH) 2 ) system is a promising energy storage technology with relatively high energy density and low cost. However, the existing models available to predict the system’s internal states are computationally expensive. An accurate and real-time capable model is therefore still required to improve its operational control. In this work, we implement a Physics-Informed Neural Network (PINN) to predict the dynamics of the TCES internal state. Our proposed framework addresses three physical aspects to build the PINN: (1) we choose a Nonlinear Autoregressive Network with Exogeneous Inputs (NARX) with deeper recurrence to address the nonlinear latency; (2) we train the network in closed-loop to capture the long-term dynamics; and (3) we incorporate physical regularisation during its training, calculated based on discretized mole and energy balance equations. To train the network, we perform numerical simulations on an ensemble of system parameters to obtain synthetic data. Even though the suggested approach provides results with the error of 3.96 × 10 − 4 which is in the same range as the result without physical regularisation, it is superior compared to conventional Artificial Neural Network (ANN) strategies because it ensures physical plausibility of the predictions, even in a highly dynamic and nonlinear problem. Consequently, the suggested PINN can be further developed for more complicated analysis of the TCES system.

Keywords: physics inspired neural network; physics-based regularisation; artificial neural network; nonlinear autoregressive network with exogenous input (NARX); thermochemical energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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