A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization

Sizhou Sun,Feng Zhu,Jingqi Fu,Nan Xiong

doi:10.1155/2018/9287097

Abstract

The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

Highlights

Owing to the abundant, renewable, and economical characteristics, the exploitation and utilization technique of renewable wind energy have attracted extensive attention of the scientific researchers
The optimization objectives including input variables binary feature selection as well as the real-value kernel parameters and weighted coefficients in multikernel function least square support vector machine (MKLSSVM) are dealt simultaneously; we develop a hybrid Gravitational Search Algorithm (GSA) combing binary-value GSA (BGSA) for feature selection with standard GSA for parameter optimization
In an effort to evaluate comprehensively the proposed combined model, the comparisons and analysis of ensemble empirical mode decomposition (EEMD)-hybrid gravitation search algorithm (HGSA)-least square support vector machine (LSSVM) based on radial basis function (RBF), Poly and multikernel function, EMD-HGSA-MKLSSVM, Wavelet Transform (WT)-HGSA-MKLSSVM, HGSA-MKLSSVM, and EEMD-MKLSSVM are given and these comparisons are divided into three parts, namely, experiments I, II, and III

Summary

Introduction

Renewable, and economical characteristics, the exploitation and utilization technique of renewable wind energy have attracted extensive attention of the scientific researchers. Wind energy has been considered as an effective way to address the global energy demands and eliminate green-house gas emissions [1]. World Wind Energy Association reports that the total installed wind turbine capacity of the top 10 countries by the end of 2016 has approximately amounted to 410.613 GW and all the wind turbines worldwide by mid-2016 can generate about 4.7% of the global electricity demand [2]. Accurate short-term wind power output forecasting has been considered as one of the most economical and effective approaches to eliminate these problems; wind speed forecasting is a fundamental task in the routine operation management of wind farms [2,3,4]

Methods

Results

Conclusion