A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting

Jianzhong Zhou, Na Sun, Benjun Jia and Tian Peng
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Jianzhong Zhou: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Na Sun: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Benjun Jia: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Tian Peng: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2018, vol. 11, issue 7, 1-27

Abstract: Due to inherent randomness and fluctuation of wind speeds, it is very challenging to develop an effective and practical model to achieve accurate wind speed forecasting, especially over large forecasting horizons. This paper presents a new decomposition-optimization model created by integrating Variational Mode Decomposition (VMD), Backtracking Search Algorithm (BSA), and Regularized Extreme Learning Machine (RELM) to enhance forecasting accuracy. The observed wind speed time series is firstly decomposed by VMD into several relative stable subsequences. Then, an emerging optimization algorithm, BSA, is utilized to search the optimal parameters of the RELM. Subsequently, the well-trained RELM is constructed to do multi-step (1-, 2-, 4-, and 6-step) wind speed forecasting. Experiments have been executed with the proposed method as well as several benchmark models using several datasets from a widely-studied wind farm, Sotavento Galicia in Spain. Additionally, the effects of decomposition and optimization methods on the final forecasting results are analyzed quantitatively, whereby the importance of decomposition technique is emphasized. Results reveal that the proposed VMD-BSA-RELM model achieves significantly better performance than its rivals both on single- and multi-step forecasting with at least 50% average improvement, which indicates it is a powerful tool for short-term wind speed forecasting.

Keywords: wind speed forecasting; hybrid forecasting model; signal decomposition techniques; parameter optimization algorithms (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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