Exploratory Data Analysis and Energy Predictions With Advanced AI and ML Techniques

Abstract

Solar and wind energy forecasting is vital for efficient energy management and sustainable power grid operations. This chapter explores machine learning (ML) algorithms for solar and wind energy forecasting using a dataset comprising power generation data and relevant environmental parameters. The Random Forest model demonstrates robust accuracy, signifying its potential for precise wind power prediction. The SVR model also performs well, affirming its aptitude for accurate wind power prediction. However, the XGBoost model stands out, achieving the lowest MAE, minimal RMSE, and exceptionally high R-squared values. These findings showcase the effectiveness of ML algorithms in harnessing data-driven insights for precise solar and wind energy forecasting, contributing to a sustainable and reliable energy future.