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Energies 2015, 8(7), 6585-6607;

Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm

* and
Department of Business Administration, North China Electric Power University, Baoding 071000, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Received: 25 May 2015 / Revised: 17 June 2015 / Accepted: 17 June 2015 / Published: 30 June 2015
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Affected by various environmental factors, wind speed presents high fluctuation, nonlinear and non-stationary characteristics. To evaluate wind energy properly and efficiently, this paper proposes a modified fast ensemble empirical model decomposition (FEEMD)-bat algorithm (BA)-least support vector machines (LSSVM) (FEEMD-BA-LSSVM) model combined with input selected by deep quantitative analysis. The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) with one residual series. Then a LSSVM is built to forecast these sub-series. In order to select input from environment variables, Cointegration and Granger causality tests are proposed to check the influence of temperature with different leading lengths. Partial correlation is applied to analyze the inner relationships between the historical speeds thus to select the LSSVM input. The parameters in LSSVM are fine-tuned by BA to ensure the generalization of LSSVM. The forecasting results suggest the hybrid approach outperforms the compared models. View Full-Text
Keywords: wind speed forecasting; LSSVM; BA; FEEMD; Granger causality test wind speed forecasting; LSSVM; BA; FEEMD; Granger causality test

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Sun, W.; Liu, M.; Liang, Y. Wind Speed Forecasting Based on FEEMD and LSSVM Optimized by the Bat Algorithm. Energies 2015, 8, 6585-6607.

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