Dynamic Simulation of Phase Change Material-Integrated Solar Water Heating Systems: A Machine Learning Approach to Energy Conversion Optimization

Abstract

Phase change material (PCM) integrated solar water heating systems represent a critical technology for sustainable energy applications, yet face significant performance limitations due to poor thermal conductivity and lack of intelligent control optimization. This study aims to develop and validate a novel machine learning-driven optimization control technique for PCM-based solar water heating systems. The methodology employs a comprehensive three-phase mathematical model encompassing pre-melting, melting transition, and post-melting thermal dynamics, coupled with a neural network controller operating on real-time environmental data to predict optimal pump flow multipliers. Comprehensive simulation validation across five environmental conditions and three PCM materials demonstrated consistent performance improvements with energy storage enhancements of 2.5-4.1% (3.3% average) and heat transfer enhancement ratios of 1.03-1.04×. This research provides the first complete ML-based control system for PCM thermal energy storage with retrofit-compatible optimization requiring no hardware modifications, offering a quantifiable performance benefit for existing installations.