A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

Wenlong Fu, Kai Wang, Jianzhong Zhou, Yanhe Xu, Jiawen Tan and Tie Chen
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Wenlong Fu: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Kai Wang: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Jianzhong Zhou: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yanhe Xu: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Jiawen Tan: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Tie Chen: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China

Sustainability, 2019, vol. 11, issue 6, 1-24

Abstract: Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.

Keywords: multi-step wind speed forecasting; variational mode decomposition; multi-scale dominant ingredient; singular spectrum analysis; phase space reconstruction; improved hybrid GWO-SCA; kernel extreme learning machine; synchronous optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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