Predicting of the short term wind speed by using a real valued genetic algorithm based least squared support vector machine

Chi Yo Huang, Bo Yu Chiang, Shih Yu Chang, Gwo Hshiung Tzeng, Chun Chieh Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

The possible future energy shortage has become a very serious problem in the world. An alternative energy which can replace the limited reservation of fossil fuels will be very helpful. The wind has emerged as one of the fastest growing and most important alternative energy sources during the past decade. However, the most serious problem being faced by human beings in wind applications is the dependence on the volatility of the wind. To apply the wind power efficiently, predictions of the wind speed are very important. Thus, this paper aims to precisely predict the short term regional wind speed by using a real valued genetic algorithm (RGA) based least squared support vector machine (LS-SVM). A dataset including the time, temperature, humidity, and the average regional wind speed being measured in a randomly selected date from a wind farm being located in Penghu, Taiwan was selected for verifying the forecast efficiency of the proposed RGA based LS-SVM. In this empirical study, prediction errors of the wind turbine speed are very limited. In the future, the proposed forecast mechanism can further be applied to the wind forecast problems based on various time spans.

Original languageEnglish
Title of host publicationIntelligent Decision Technologies - Proceedings of the 3rd International Conference on Intelligent Decision Technologies, IDT'2011
Pages567-575
Number of pages9
DOIs
Publication statusPublished - 2011 Dec 1
Event3rd International Conference on Intelligent Decision Technologies, IDT'2011 - Piraeus, Greece
Duration: 2011 Jul 202011 Jul 22

Publication series

NameSmart Innovation, Systems and Technologies
Volume10 SIST
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Other

Other3rd International Conference on Intelligent Decision Technologies, IDT'2011
CountryGreece
CityPiraeus
Period11/7/2011/7/22

Keywords

  • Genetic algorithm (GA)
  • Least squared support vector machine (LS-SVM)
  • Short term wind prediction
  • Support vector machines (SVMs)
  • Wind power
  • Wind speed forecasting

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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  • Cite this

    Huang, C. Y., Chiang, B. Y., Chang, S. Y., Tzeng, G. H., & Tseng, C. C. (2011). Predicting of the short term wind speed by using a real valued genetic algorithm based least squared support vector machine. In Intelligent Decision Technologies - Proceedings of the 3rd International Conference on Intelligent Decision Technologies, IDT'2011 (pp. 567-575). (Smart Innovation, Systems and Technologies; Vol. 10 SIST). https://doi.org/10.1007/978-3-642-22194-1_56