Development and trending of deep learning methods for wind power predictions

Abstract With the increasing data availability in wind power production processes due to advanced sensing technologies, data-driven models have become prevalent in studying wind power prediction (WPP) methods. Deep learning models have gained popularity in recent years due to their ability of handling high-dimensional input, automating data feature engineering, and providing high flexibility in modeling. However, with a large volume of deep learning based WPP studies developed in recent literature, it is important to survey the existing developments and their contributions in solving the issue of wind power uncertainty. This paper revisits deep learning-based wind power prediction studies from two perspectives, deep learning-enabled WPP formulations and developed deep learning methods. The advancement of WPP formulations is summarized from the following perspectives, the considered input and output designs as well as the performance evaluation metrics. The technical aspect review of deep learning leveraged in WPPs focuses on its advancement in feature processing and prediction model development. To derive a more insightful conclusion on the so-far development, over 140 recent deep learning-based WPP studies have been covered. Meanwhile, we have also conducted a comparative study on a set of deep models widely used in WPP studies and recently developed in the machine learning community. Results show that DLinear obtains more than 2% improvements by benchmarking a set of strong deep learning models. Potential research directions for WPPs, which can bring profound impacts, are also highlighted.