Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review - Alkabbani, Hanin; Ahmadian, Ali; Zhu, Qinqin; Elkamel, Ali (Frontiers in Chemical Engineering, 2021)
The global trend towards a green sustainable future encouraged the penetration of renewable energies into the electricity sector to satisfy the various market demands. Successfully and economically, steadily integrations of renewables into the microgrids necessitate building reliable, accurate wind and solar power forecasters adopting these renewables’ stochastic behaviors. In the literature, Machine learning (ML)-based forecasters have been widely utilized for wind and solar power forecasting with reported promising and accurate results. The objective of this article is to provide a critical systematic review of existing wind and solar power ML forecasters, particularly artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), and extreme learning machines (ELM). Besides, special attention is paid to the metaheuristics accompanied by these ML models. Detailed comparisons between the different ML methodologies and the metaheuristics techniques are performed. The significant drawn-out findings from the reviewed papers are also summarized based on the forecasting targets and horizons in tables. Finally, challenges and future directions for research on ML solar and wind prediction methods are presented. This review can guide scientists and engineers in analyzing and selecting the appropriate prediction approaches based on the different circumstances and applications.