A multi-layer extreme learning machine refined by sparrow search algorithm and weighted mean filter for short-term multi-step wind speed forecasting. (March 2022)

• Record Type: Journal Article
• Authors: Zhang, Haochen; Peng, Zhiyun; Tang, Junjie; Dong, Ming; Wang, Ke; Li, Wenyuan
• Is Part Of: Sustainable energy technologies and assessments. Volume 50(2022)
• Publication Date: 2022-03
• Subjects: Wind speed forecasting -- Weighted mean filter -- Multi-layer extreme learning machine -- Sparrow search algorithm -- Ensemble model; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101698
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Boundary effects of wavelet threshold denoising methods are disclosed and analyzed. Boundary effects are overcome by adoption of Weighted mean filter (WMF). Multi-layer extreme learning machine (MLELM) is conducted in combination with WMF. Hidden layers of MLELM are refined and optimized by sparrow search algorithm (SSA). The ensemble model WMF-SSA-MLELM achieves a good performance in accuracy and speed. Abstract: With a rapidly growing wind power capacity, wind speed forecasting is of great importance for secure and economical operation of power systems. Nonetheless, due to the volatility of wind speed, its prediction has always been a challenging task. Although utilization of promising noise reduction techniques can mitigate this problem to some extent, wavelet threshold denoising methods (WTDs) suffer from their boundary effects in practice. To address this issue, weighted mean filtering (WMF) is adopted as an alternative of WTDs to reduce the information redundancy of wind speed time series. Furthermore, multi-layer extreme learning machine (MLELM) is combined with WMF to form an ensemble model for one-step and multi-step wind speed forecasting. Besides, as a novel swarm algorithm, the sparrow search algorithm (SSA), is utilized to improve the performance of MLELM. Finally, a data-driven ensemble model WMF-SSA-MLELM is proposed herein, which can be divided into three blocks, data preprocessing, optimization, and prediction. In the first block, WMF suppresses redundant noise in wind speed time series and makes it easier to extract essential features of wind speed. In the second block, SSA optimizes input weights and biases of hidden layers in the optimization stage. In the prediction block, MLELM with two hidden layers optimized by SSA provides the prediction of future wind speed by using denoised wind speed series obtained in the first two steps. The test results on four datasets demonstrate superior accuracy and efficiency of the proposed model WMF-SSA-MLELM, via comparison with all candidate models in both one-step and multi-step wind speed forecasting.