Short-Term Forecasts of Energy Generation in a Solar Power Plant Using Various Machine Learning Models, along with Ensemble and Hybrid Methods

Abstract

High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods.

Keywords

• ensemble forecasting methods
• feature engineering
• feature importance
• machine learning
• short-term forecasting
• solar power plant