 Data mining and wind power prediction: A literature reviewIlhami Colak, Seref Sagiroglu and Mehmet Yesilbudak Renewable Energy, 2012, vol. 46, issue C, 241-247 Abstract: Wind power generated by wind turbines has a non-schedulable nature due to the stochastic nature of meteorological conditions. Hence, wind power predictions are required for few seconds to one week ahead in turbine control, load tracking, pre-load sharing, power system management and energy trading. In order to overcome problems in the predictions, many different wind power prediction models have been used to achieve in the literature. Data mining and its applications have more attention in recent years. This paper presents a review study banned on very short-term, short-term, medium-term and long-term wind power predictions. The studies available in the literature have been evaluated and criticized in consideration with their prediction accuracies and deficiencies. It is shown that adaptive neuro-fuzzy inference systems, neural networks and multilayer perceptrons give better results in wind power predictions. Keywords: Data mining; Data mining techniques; Wind power prediction; Prediction time scales and models; Literature evaluation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:46:y:2012:i:c:p:241-247 DOI: 10.1016/j.renene.2012.02.015 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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