Short term wind speed prediction based on evolutionary support vector regression algorithms
| Autor: | Ángel M. Pérez-Bellido, Antonio Portilla-Figueras, Luis Prieto, Sancho Salcedo-Sanz, Emilio G. Ortiz-García |
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| Rok vydání: | 2011 |
| Předmět: |
Mathematical optimization
Wind power business.industry Computer science Computer Science::Neural and Evolutionary Computation General Engineering Evolutionary algorithm Particle swarm optimization Machine learning computer.software_genre Wind speed Evolutionary computation Computer Science Applications Term (time) Support vector machine Artificial Intelligence Artificial intelligence business computer Algorithm Evolutionary programming |
| Zdroj: | Expert Systems with Applications. 38:4052-4057 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2010.09.067 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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