INTELLIGENT RENEWABLE ENERGY MANAGEMENT WITH AUTOMATED SOLAR-GRID SWITCHING FOR UNINTERRUPTED AND EFFICIENT POWER SUPPLY

Abstract

The intermittent nature of renewable energy sources (RES) like solar power poses significant challenges for grid stability and energy reliability. Conventional systems often rely on manual or rule-based switching between solar and grid power, leading to inefficiencies, energy wastage, and power interruptions. This paper proposes an AI-driven Renewable Energy Management System (REMS) that integrates automated solar-grid switching, real-time load forecasting, and dynamic energy optimization to ensure uninterrupted, cost-effective, and sustainable power supply. The system employs IoT sensors to monitor solar generation, grid availability, and load demand, while a machine learning (ML) algorithm predicts energy requirements and triggers seamless transitions between solar and grid sources. Key innovations include a hybrid inverter with bidirectional power flow, a battery storage system for excess solar energy, and a cloud-based dashboard for remote control. Field testing demonstrated a 25–35% reduction in grid dependency, 95% power continuity during outages, and 20% lower energy costs compared to traditional systems. By prioritizing solar energy during peak generation and intelligently switching to the grid during deficits, the system optimizes resource utilization while reducing carbon footprints. This research highlights the transformative potential of AI and automation in advancing resilient and sustainable energy infrastructures.

Author

Mr. Saravanan S, Mr.Vairasamy P, Mr.Baskaran P, Mr.Logeshwaran R