Supervised Machine Learning for Renewable Energy Forecasting

Abstract

Renewable energy forecasting is a critical challenge in the transition to sustainable energy systems. The variability of renewable energy sources such as solar and wind necessitates accurate forecasting methods to enhance grid stability, optimize energy distribution, and reduce reliance on fossil fuels. Supervised machine learning (ML) techniques have emerged as powerful tools for addressing these challenges, leveraging historical data to predict energy generation with improved accuracy. This paper provides an in-depth analysis of supervised machine learning models applied to renewable energy forecasting, covering methodologies, challenges, and experimental results. Various algorithms, including decision trees, support vector machines (SVM), artificial neural networks (ANN), and ensemble models, are explored in the context of energy prediction. A comprehensive experiment is conducted using real-world datasets, comparing model performance based on accuracy metrics such as mean absolute error (MAE) and root mean square error (RMSE). The results demonstrate that ensemble methods and deep learning-based approaches outperform traditional statistical techniques, highlighting the importance of advanced ML strategies. This research contributes to the ongoing development of efficient renewable energy forecasting methods, ultimately supporting the integration of clean energy into power systems.