Wind speed forecasting based on FEEMD and LSSVM optimized by the bat algorithm

45Citations
Citations of this article
29Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Sun, W., Liu, M., & Liang, Y. (2015). Wind speed forecasting based on FEEMD and LSSVM optimized by the bat algorithm. Energies, 8(7), 6585–6607. https://doi.org/10.3390/en8076585

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free