Smart Rural Energy Networks using AI-based Forecasting System

The increasing penetration of renewable energy resources in rural electrification, combined with decentralized microgrid deployment, demands intelligent forecasting and management frameworks to ensure reliability, efficiency, and sustainability. This paper proposes an AI-based system for forecasting renewable energy generation and load demand, and for dynamically managing smart rural energy networks. The framework integrates machine learning (ML) and deep learning (DL) models for generation and demand forecasting, and leverages adaptive scheduling and optimization techniques for load balancing, storage management, and dispatch control. The proposed approach aims to improve energy availability, reduce dependence on backup power, optimize storage utilization, and support sustainable rural electrification. Simulation results (or expected outcomes) indicate that AIdriven forecasting can significantly reduce prediction error, and adaptive management can enhance energy reliability and minimize curtailment. The study underscores the potential of AIenabled microgrids for empowering rural communities with dependable, clean energy access.