Short-term wind speed forecasting based on two-stage preprocessing method, sparrow search algorithm and long short-term memory neural network

• Published in: Energy reports Vol. 8; pp. 14997 - 15010
• Main Authors: Ai, Xueyi, Li, Shijia, Xu, Haoxuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022; Elsevier
• Subjects: Long short-term memory; Sample entropy; Short-term wind speed prediction; Singular spectrum analysis; Sparrow search algorithm; Variational mode decomposition; Singular spectrum analysis; Sample entropy; Long short-term memory; Variational mode decomposition; Sparrow search algorithm; Short-term wind speed prediction
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2022.11.051

Wind energy, as an environment-friendly and renewable energy source, has become one of the most effective alternatives to conventional power sources. However, the intermittent nature of wind speed and the interference of noise signal bring several challenges to the safety and reliability of power grid operation. To tackle this issue, a two-stage preprocessing strategy is designed, and the short-term wind speed prediction model based on long short-term memory (LSTM) is proposed. Firstly, singular spectrum analysis (SSA) is introduced to extract the target data and filter the noise data. Next, the denoised sequence is decomposed by variational mode decomposition (VMD) into multiple intrinsic mode functions (IMFs), which are further aggregated by sample entropy (SE). Besides, the hyper-parameters of LSTM neural network are optimized by the newly sparrow search algorithm (SPSA) possessing excellent global optimization ability. Subsequently, the aggregated sequences are coupled with the SPSA-LSTM modules synchronously. The ultimate wind speed forecasting results are obtained by superimposing the predicted values of all sequences. In order to evaluate the effectiveness of proposed approach, two case studies are conducted based on two datasets collected from different sites with 10-min and 1-hour intervals by comparing seven relevant models. The experimental results demonstrate that the proposed SSA-VMD-SE-SPSA-LSTM can adequately extract the inherent features of wind speed series, thus achieving higher prediction accuracy.