Renewable Energy Forecasting with Machine Learning Models

Abstract

This research investigates the use of machine learning models to forecast renewable energy production. By analyzing historical weather data and current renewable energy outputs, the study demonstrates how machine learning algorithms can accurately predict energy generation from sources like wind and solar. The findings reveal how improved forecasting models can facilitate better grid management and resource allocation, ultimately supporting the integration of renewable energy into existing power systems. This study underscores the importance of intelligent data analysis in enhancing the reliability and efficiency of renewable energy systems.