AUTHOR=Liu Yaxin , Wang Yunjing , Wang Qingtian , Zhang Kegong , Qiang Weiwei , Wen Qiuzi Han TITLE=Recent advances in data-driven prediction for wind power JOURNAL=Frontiers in Energy Research VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1204343 DOI=10.3389/fenrg.2023.1204343 ISSN=2296-598X ABSTRACT=

Wind power is one of the most representative renewable energy and has attracted wide attention in recent years. With the increasing installed capacity of global wind power, its nature of randomness and uncertainty has posed a serious risk to the safe and stable operation of the power system. Therefore, accurate wind power prediction plays an increasingly important role in controlling the impact of the fluctuations of wind power to in system dispatch planning. Recently, with the rapid accumulation of data resource and the continuous improvement of computing power, data-driven artificial intelligence technology has been popularly applied in many industries. AI-based models in the field of wind power prediction have become a cutting-edge research subject. This paper comprehensively reviews the AI-based models for wind power prediction at various temporal and spatial scales, covering from wind turbine level to regional level. To obtain in-depth insights on performance of various prediction methods, we review and analyze performance evaluation metrics of both deterministic models and probabilistic models for wind power prediction. In addition, challenges arising in data quality control, feature engineering, and model generalization for the data-driven wind power prediction methods are discussed. Future research directions to improving the accuracy of data-driven wind power prediction are also addressed.