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Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models
Abstract
Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems.
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Citations
Cited by
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Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., & Hu, Z. (2015) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2015.00046 · Cited by 654
Review on probabilistic forecasting of wind power generation
Zhang, Y., Wang, J., & Wang, X. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.033 · Cited by 646
A review on the young history of the wind power short-term prediction
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., & Feitosa, E. (2008) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2007.01.015 · Cited by 638
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Metadata
- Authors
- Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H. A., & Nielsen, T. S.
- Venue
- Wind Engineering
- Publisher
- SAGE Publications
- Published
- 2005-12
- Volume
- 29
- Issue
- 6
- Pages
- 475-489
- ISSN
- 0309-524X (ISSN-L)
- 2048-402X
- OpenAlex
- W2105281013
- Cited by
- 343
- References
- 0
- FWCI
- 13.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Madsen, H., Pinson, P., Kariniotakis, G., Nielsen, H. A., & Nielsen, T. S. (2005). Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models. Wind Engineering. https://doi.org/10.1260/030952405776234599
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