Short-term prediction of wind power with a clustering approach

Andrew Kusiak and Wenyan Li

Renewable Energy, 2010, vol. 35, issue 10, 2362-2369

Abstract: A clustering approach is presented for short-term prediction of power produced by a wind turbine at low wind speeds. Increased prediction accuracy of wind power to be produced at future time periods is often bounded by the prediction model complexity and computational time involved. In this paper, a trade-off between the two conflicting objectives is addressed. First, a set of the most relevant parameters (predictors) is selected using the underlying physics and pattern immersed in data. Five scenarios of the input space are created with the k-means clustering algorithm. The most promising clustering scenario is applied to produce a model for each clustered subspace. Computational results are compared and the benefits of cluster–specific (customized) models are discussed. The results show that the prediction accuracy is improved the input space is clustered and customized prediction models are developed.

Keywords: Wind turbine; Wind energy; Data-mining; Clustering; Power prediction; Parameter selection (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148110001448
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:35:y:2010:i:10:p:2362-2369

DOI: 10.1016/j.renene.2010.03.027

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().