Two-phase deep learning model for short-term wind direction forecasting. (August 2021) - Tang, Zhenhao; Zhao, Gengnan; Ouyang, Tinghui
Abstract: Accurate and reliable wind direction prediction is important for improving wind power conversion efficiency and operation safety. In this paper, a two-phase deep learning model is proposed and constructed for high-performance short-term wind direction forecasting. In the first phase, a hybrid data processing strategy, including data reconstruction, outlier deletion, dimension reduction, and sequence decomposition, is proposed to extract the most meaningful information from practical data. Then, in the second phase, a robust echo state network is developed for wind direction forecasting. In addition, its hyper-parameters are optimized using an improved flower pollination algorithm (IFPA) to achieve high efficiency. Experiments conducted on data from real wind farms validate the proposed hybrid data processing method. Finally, comparisons with benchmark prediction models show that the proposed network achieves superior performance. Highlights: A two-phase short-term wind direction prediction model is proposed. Hybrid data processing method is used to extract data's most meaningful information. Improved echo state network (ESN) is developed in prediction modeling. An improved flower pollination algorithm is proposed to optimize ESN's parameters. The proposed model is validated efficient and effective in wind direction prediction.