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Energies 2018, 11(7), 1752; https://doi.org/10.3390/en11071752

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

1,2
,
1,2,* , 1,2
and
1,2
1
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2
Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 1 June 2018 / Revised: 26 June 2018 / Accepted: 27 June 2018 / Published: 4 July 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
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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. View Full-Text
Keywords: wind speed forecasting; hybrid forecasting model; signal decomposition techniques; parameter optimization algorithms wind speed forecasting; hybrid forecasting model; signal decomposition techniques; parameter optimization algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhou, J.; Sun, N.; Jia, B.; Peng, T. A Novel Decomposition-Optimization Model for Short-Term Wind Speed Forecasting. Energies 2018, 11, 1752.

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