An effective predictor of the dynamic operation of latent heat thermal energy storage units based on a non-linear autoregressive network with exogenous inputs

Thermal networks require thermal energy storage (TES) provisions for balancing thermal energy sources with variable consumer demand. Harvesting ice is an economical option for latent heat TES systems in cooling networks given the wide availability of the storage medium. This paper presents an artificial intelligence (AI) based model to monitor the state-of-charge (SoC) and the outlet temperature of the heat transfer fluid (To) of an ice tank under fluctuating operating conditions. The AI model is a non-linear autoregressive network with exogenous inputs (NARX) that was trained and tested with datasets obtained from experimental measurements of a practical ice tank and a physics-based model of the tank. The NARX model was sensitised with physics-informed attributes to recognise different heating and cooling zones. The model exhibits a high accuracy in predicting the operating conditions of the ice tank when benchmarked against both experimental measurements of a practical tank and outputs from the physics-based model. For instance, it achieves R2 values of 0.9943 and 0.9842 for SoC and To, with root mean square errors of 1.73% for SoC and 0.3161°C for To. The NARX model is 86% faster than its physics-based counterpart and its implementation requires limited computational resources—making it suitable as a standalone estimator for the TES operation and the accelerated simulation of energy systems containing latent heat TES units. Furthermore, given the limited availability of NARX models in open-source libraries, the presented NARX model and relevant datasets have been made available alongside this paper to contribute to open-science in energy research and the broader AI community.