Sparse Variational Gaussian Processes model for prediction energy parameters in solar power plant and wind farm

This study applies Sparse Variational Gaussian Processes (SVGP) for probabilistic forecasting of energy output from a solar plant and a wind farm with eight turbines.Trained on two years of hourly SCADA data, the SVGP model-combining a neural mean function and kernel-based covariance-produces accurate forecasts with confidence intervals over 24-120 hour horizons.Results confirm its scalability and precision thanks to evaluation metrics such as MAE, RMSE, CRPS, and R 2 across renewable energy types.