Integrating Computational and Experimental Methods for Thermal Energy Storage: A Predictive Artificial Neural Network Model for Cold and Hot Sensible Systems

This study introduces a predictive model based on artificial neural networks (ANNs) for estimating the dynamic performance of commercially available sensible thermal energy storage (STES) systems. The model was trained and validated using high-resolution experimental data measured from two vertical cylindrical tanks (0.3 m3 each) including internal heat exchangers and operating under both heating and cooling modes. A comprehensive sensitivity analysis was conducted on 28 ANN architectures by varying the number of hidden neurons and input delays. The optimal configuration, designated as ANN5 (12 neurons, delay = 1), demonstrated superior accuracy in predicting temperature profiles and energy exchange. Validation against an independent dataset confirmed the model’s robustness, achieving normalized root mean square errors (NRMSEs) between 0.0022 and 0.0061 for the hot tank and between 0.0057 and 0.0283 for the cold tank. Energy prediction errors were within −3.87% for charging and 0.09% for discharging in heating mode, and 7.08% for charging and 0.13% discharging in cooling mode, respectively. These results highlight the potential of ANN-based approaches for real-time control, forecasting, and digital twin applications in STES systems.