A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network

As the energy crisis intensifies, wind energy generated by wind turbines, commonly known as a promising renewable energy source, is being more frequently employed. As a result, wind energy forecasting, particularly wind speed forecasting, plays a crucial role for wind energy management. Due to their importance, many wind speed forecasting methods have been proposed. However, many of the traditional prediction models did not account for data pre-processing, or the constraints of an individual forecasting model, resulting in poor prediction accuracy. The purpose of this article is to present a unique forecasting model that incorporates noise-processing methods, statistical techniques, deep learning frameworks, and optimization algorithms to improve on existing methods. The suggested ensemble model was tested using 10-minute wind speed data from real-world conditions. The experimental results show that the mean absolute percentage error of the 10-minute prediction of the proposed model is 5.73%, which is also about 17% improvement compared to the competing model (mean absolute percentage error of 6.71%).