Machine Learning for Renewable Energy Forecasting and Smart Energy Management

Abstract

The increasing adoption of renewable energy sources has created new challenges related to energy forecasting, grid stability, demand management, and efficient utilization of distributed energy resources. Machine Learning (ML) provides advanced analytical capabilities for addressing these challenges by learning complex relationships from historical and real-time energy data. This review examines the application of ML and Artificial Intelligence (AI) techniques in renewable energy forecasting and smart energy management. Particular attention is given to solar power forecasting, wind power prediction, electricity demand forecasting, energy consumption optimization, and fault detection in renewable energy systems. Traditional machine learning algorithms, ensemble methods, artificial neural networks, deep learning, and hybrid forecasting models are compared in terms of their capabilities and limitations. The integration of ML with Internet of Things (IoT) sensors, smart meters, edge computing, and smart grids is also discussed. Major challenges related to data quality, weather uncertainty, computational complexity, model generalization, and real-time deployment are identified. The review concludes by highlighting the potential of explainable AI, federated learning, digital twins, and edge intelligence for developing reliable, efficient, and sustainable intelligent energy systems.