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

Energies

Wei Sun¹  Mohan Liu¹  Yi Liang¹ 

关键词: wind speed forecasting;    LSSVM;    BA;    FEEMD;    Granger causality test;   

DOI  :  10.3390/en8076585

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.