Intelligent Hybrid Solar Energy Management System for Water Heating and Backup with AI-Driven Resource Optimization

Abstract

This research work describes an innovative Hybrid Solar Energy Management System for DC Microgrids with Low-Voltage. The system is integrated to provide an Energy Management System in conjunction with Solar Generation, Battery Storage and Control for Thermal Loads. There is a predictive framework for short-term sun light forecasting and battery State of Charge (SOC) based on real-time electric and thermal sensors that use machine learning (ML). The system does not utilize traditional threshold-based control systems, but rather uses predictive analytics to dynamically manage the energy usage of the system and regulate resistive heating loads from high-current resistive heating sources, thus providing benefits associated with reduced battery stress and increased overall system performance. Data from real-world/hardware model and simulation testing demonstrate that the system provides improved forecasting ability and better SOC stability as compared to previous systems under varying environmental conditions. The proposed research will reduce peak discharge current through the battery, minimize excessive battery degradation and allow for more reliable use of electric energy in residential and off-grid applications.