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A Review of Wind Power Forecasting Models

Open access
Wang, Xiaochen, Guo, Peng, Huang, Xiaobin·2011·Energy Procedia

Abstract

Rapid growth in wind power, as well as increase on wind generation, requires serious research in various fields. Because wind power is weather dependent, it is variable and intermittent over various time-scales. Thus accurate forecasting of wind power is recognized as a major contribution for reliable large-scale wind power integration. Wind power forecasting methods can be used to plan unit commitment, scheduling and dispatch by system operators, and maximize profit by electricity traders. In addition, a number of wind power models have been developed internationally, such as WPMS, WPPT, Prediktor, ARMINES, Previento, WPFS Ver1.0 etc. This paper provides a review on comparative analysis on the foremost forecasting models, associated with wind speed and power, based on physical methods, statistical methods, hybrid methods over different time-scales. Furthermore, this paper gives emphasis on the accuracy of these models and the source of major errors, thus problems and challenges associated with wind power prediction are presented.

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Metadata

Authors
Wang, X., Guo, P., & Huang, X.
Venue
Energy Procedia
Publisher
Elsevier BV
Published
2011
Volume
12
Pages
770-778
ISSN
1876-6102 (ISSN-L)
OpenAlex
W2067718885
Cited by
443
References
1
FWCI
13.48
Type
conference paper
Language
en
Open access
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CoChat. (2026). Citation check: A Review of Wind Power Forecasting Models. https://app.cochat.ai/papers/10.1016/j.egypro.2011.10.103
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Wang, X., Guo, P., & Huang, X. (2011). A Review of Wind Power Forecasting Models. Energy Procedia. https://doi.org/10.1016/j.egypro.2011.10.103

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.