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Advances in Meteorology
Volume 2017 (2017), Article ID 6856139, 22 pages
https://doi.org/10.1155/2017/6856139
Research Article

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China

Correspondence should be addressed to Xiaohui Yuan and Xiaohui Lei

Received 1 August 2017; Revised 6 November 2017; Accepted 14 November 2017; Published 12 December 2017

Academic Editor: Ilan Levy

Copyright © 2017 Aiqing Kang et al. 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.

Abstract

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.