Ultra-Short-Term Wind Speed Forecasting Using the Hybrid Model of Subseries Reconstruction and Broad Learning System

Ming Pang, Lei Zhang, Yajun Zhang, Ao Zhou, Jianming Dou and Zhepeng Deng
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Ming Pang: School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Lei Zhang: School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an 710048, China
Yajun Zhang: School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Ao Zhou: School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Jianming Dou: School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Zhepeng Deng: School of Chemical and Chemical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2022, vol. 15, issue 12, 1-21

Abstract: The traditional decomposition–combination wind speed forecasting model has high complexity and a long calculation time. As a result, an ultra-short-term wind speed hybrid forecasting model based on a broad learning system (BLS) that combines improved variational mode decomposition (EPSO-VMD, EVMD) and subseries reconstruction (SR) is proposed in this work. The values of K and α in the EVMD are determined by minimum mean envelope entropy (MMEE) and enhanced particle swarm optimization (EPSO), and EVMD is used to decompose the original wind speed data. SR is applied to recombine the subseries obtained by EVMD to improve the forecasting efficiency. The sample entropy (SE) is used to quantify the subseries’ complexity, and they are then adaptively divided into high-entropy and low-entropy subseries. Adjacent high-entropy subseries of approximate entropy values are merged to obtain a new group of reconstructed high-entropy subseries, while the low-entropy subseries merge into a new subseries as well. Then, the forecasting results of the reconstructed high- and low-entropy subseries are calculated via the BLS and ARIMA models. Numerical simulation results show that the proposed method is more effective than traditional methods.

Keywords: ultra-short-term wind speed forecasting; broad learning system; variational mode decomposition; subseries reconstruction; sample entropy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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