Hybrid and ensemble models for solar energy forecast

Hybrid and ensemble forecasting models offer a synergistic approach to enhance forecasting accuracy by combining various modeling paradigms. These models leverage the strengths of statistical, machine learning, and physics-based methods to overcome individual limitations and achieve superior predictive performance.This chapter explores the integration of time-tested statistical methods like auto-regressive models with the adaptability and pattern recognition capabilities of machine learning models. Additionally, the incorporation of physics-based models adds domain-specific knowledge and constraints to the forecasting process.The chapter not only discusses the theoretical foundations of hybrid models but also provides practical implementation insights. It emphasizes their relevance in improving accuracy across various forecasting applications. The theoretical discussion is followed by a robust case study that demonstrates the application of a hybrid CNN and LSTM ensemble model. It utilizes infrared all-sky imager data to enhance short-term photovoltaic forecasting and incorporates additional multivariate time-series prediction for additional context-driven decisions and improved accuracy.