Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations

Wind power forecasting (WPF) is the crucial technology for power system operation with large-scale grid-connected wind farms. A large number of related studies have emerged with the development of abundant features and computer science. This research reviews the feature mining methods and the latest predictor structure to provide the latest point of view in this field. It classifies the WPF process methods into time-frequency domain analysis, feature engineering, and predictor structures. Firstly, the overall and detailed mathematical formulations are summarized to provide a more generalized research version of WPF process methods. Particularly, in each part, the logical relations of the latest models are innovatively combed based on their typical scientific problems. In addition, this research summarizes six cutting-edge predictor structures that solve critical scientific or engineering problems. Finally, several developments and challenges are discussed. Among them, multi-source data (including but not limited to NWP) and algorithms still remain a research hotspot. Meanwhile, the study believes the engineering of evaluation may provide a new research perspective. The data quality, reproducibility, data privacy, and interpretability will be the challenges and concerns. In general, this review provides a critical methods reference and inspiration for engineers from multiple perspectives.