Short‐term wind power forecasting based on two‐stage attention mechanism

Abstract

Wind power is usually closely related to the meteorological information around the wind farm, which leads to the fluctuation of wind power and makes it difficult to predict precisely. In this study, a wind power forecasting model based on long–short‐term memory network two‐stage attention mechanism is established. The attention mechanism is extensively employed to weigh the input feature and strengthen the trend characteristic of wind power. The intermittency and volatility feature of the wind are efficiently mitigated, and the prediction accuracy is improved significantly. Besides, quantile regression and kernel density estimation are combined with the proposed model to predict the wind power interval and the probability density. These two parameters are important information for ensuring security and stability while accessing to the electricity grid. The simulation results on the actual wind power dataset verify the higher prediction accuracy of the proposed model compared with other machine learning methods.