Forecasting techniques for power systems with renewables

Abstract

This chapter conducts a comprehensive analysis of renewable energy generation prediction methods, ranging from classical to contemporary approaches. Fundamental concepts of forecasting are explored, and traditional techniques, as well as meteorological models, are examined. Additionally, a deep dive into the use of machine learning and neural networks for accurately anticipating renewable energy production is presented. The review highlights the effectiveness and limitations of each method, providing a comprehensive insight into the current state of the field. The existing challenges are identified, such as the adaptability of traditional methods to the evolving energy landscape and the optimization of accuracy in meteorological models. Furthermore, the need for computational resources in machine learning approaches is addressed. Based on this analysis, future research directions are proposed. These include enhancing the adaptability of traditional methods, optimizing accuracy in meteorological models, and exploring more resource-efficient approaches in terms of computational resources. This chapter serves as a valuable guide for researchers interested in addressing current challenges and advancing the prediction of renewable energy generation.