A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression
Expert Systems with ApplicationsPublished 25 June 2008
Chih‐Hung Wu, Gwo‐Hshiung Tzeng, Rong-Ho Lin
Citations281
SJR quartileQ1
SJR score1.85
SNIP2.55
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TL;DR
The new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting.
Abstract
This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model outperforms the previous models. Specifically, the new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting.
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Decision SciencesEngineeringEnvironmental Science
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