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Short term wind speed prediction based on evolutionary support vector regression algorithms
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DOI：10.1016/j.eswa.2010.09.067.png)
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摘要
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Hyper-parameters estimation in regression Support Vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems. This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc. Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms. In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs. Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO). We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Support vector regression algorithms
Evolutionary algorithms
Hyper-parameters estimation
Short-term wind speed forecasting
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7.5
论文数:
3.0W
被引数:
10.2W
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