Analysis of Renewable Energy Forecasting Using Machine Learning Techniques

Abstract

The accelerating shift toward renewable energy systems has heightened the necessity for accurate forecasting models capable of managing the inherent variability in wind, solar and hybrid energy sources. Machine learning (ML) techniques have emerged as powerful tools for predicting renewable energy generation due to their ability to model nonlinear dynamics, adapt to evolving patterns and exploit large datasets. This study provides a comprehensive analytical assessment of ML-based forecasting approaches for renewable energy systems, covering linear regression, support vector regression, random forests, long short-term memory networks and hybrid optimization frameworks. Emphasis is placed on model performance, data preprocessing strategies, feature engineering requirements and temporal resolution considerations. The paper examines how uncertainty in meteorological variables, such as irradiance, temperature and wind speed, affects prediction accuracy and demonstrates how ML algorithms can alleviate these fluctuations through robust training and error correction schemes. Additionally, the study discusses the integration of ML forecasting tools into smart grids, illustrating their contribution to demand–supply balancing, storage scheduling and economic dispatch. The findings suggest that ML driven forecasting significantly enhances grid reliability and operational efficiency, although challenges persist concerning data scarcity, model generalization, explainability and real-time deployment. Future prospects include federated learning, physics-informed networks and multimodal forecasting platforms.