Optimization of Sand Battery Systems for Renewable Energy Storage Using Artificial Neural Networks

As renewable energy systems become increasingly intertwined globally, the need for low-cost, large-scale, and environmentally benign long-duration energy storage technologies has intensified. Silica-rich materials found in deserts have led to the implication that sand-based thermal energy storage (TES) systems should be developed in advance. Due to unavoidable heat-transfer limitations, nonuniform thermal distribution, and scepticism about the intelligent optimisation framework, system performance still operates within its limits.In this paper, we present a framework for optimizing sand battery systems using Artificial Neural Networks to improve operational efficiency. We developed a numerical thermodynamic model that simulates temperature dynamics within a silica sand bed as a function of key input parameters , including charging temperature (300–700°C), charge/discharge rate (5–25°C·min⁻¹), and coupling material type. A multilayer ANN trained on a dataset of 500 simulated scenarios generated using Latin Hypercube Sampling. As a result, the ANN demonstrated high predictive ability (MAE = 1.72%; RMSE = 2.31%; R² = 0.963). Afterward, a Genetic Algorithm (GA) was used to find operating conditions with the highest efficiency.We then derive a decision-tree model that classifies storage performance based on efficiency, retention time, energy loss rate, and cost. The Mean Performance Index (MPI) achieved was 84.1%, suggesting a high degree of overall fitness of sand-based TES for long-term storage.The combined ANN–GA–Decision-tree framework is a promising tool for improving the performance of sand batteries, paving the way for computerized, intelligent optimization and supporting economies of scale for low-cost renewable energy storage technology.