AI-Driven Solar Energy Forecasting, Optimization, and Blockchain-Based Energy Management

Abstract

The recent growth in the uptake of renewable sources of power in the contemporary power systems comes with overwhelming challenges in the energy forecasting, demand management, and the safe energy trade. This paper proposes an AI-driven renewable energy management framework that combines machine learning-based generation prediction, demand prediction, energy allocation optimization, and blockchain-based recording of transactions. Forecasting models are trained with historical data of the solar generation and weather, and Linear Regression serves as a baseline model and Random Forest is a better model to predict the generation of solar energy. Moreover, a demand forecasting module is designed to predict the energy consumption trends, which guarantees real-time supply-demand balance. An optimization engine assigns the estimated available renewable energy to local consumption, battery storage, and export to the grid taking into account operational limits including battery capacity and grid prices. A blockchain-ledger repository is adopted to document energy allocation decisions to achieve secure and transparent energy transactions. The experimental findings prove the point that the Random Forest forecasting model is highly predictive with an R2 score of about 0.98. The suggested framework also enhances the utilization of energy and allows saving economic benefits by increasing the export revenue of the grid. The findings demonstrate that the hybrid AI-based system is an efficient and safe solution in managing renewable energy in intelligent grid settings.