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Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts
Abstract
For operational planning it is important to provide information about the situation‐dependent uncertainty of a wind power forecast. Factors which influence the uncertainty of a wind power forecast include the predictability of the actual meteorological situation, the level of the predicted wind speed (due to the non‐linearity of the power curve) and the forecast horizon. With respect to the predictability of the actual meteorological situation a number of explanatory variables are considered, some inspired by the literature. The article contains an overview of related work within the field. An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the forecast error. Only explanatory variables or indices which are predictable are considered, whereby the model obtained can be used for providing situation‐dependent information regarding the uncertainty. Finally, the article contains directions enabling the reader to replicate the methods and thereby extend other forecast systems with situation‐dependent information on uncertainty. Copyright © 2005 John Wiley & Sons, Ltd.
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Cited by
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Zhang, Y., Wang, J., & Wang, X. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.033 · Cited by 646
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Self-citationEnergy Forecasting: A Review and Outlook
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., & Zareipour, H. (2020) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2020.3029979 · Cited by 621
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Metadata
- Authors
- Nielsen, H. A., Madsen, H., & Nielsen, T. S.
- Venue
- Wind Energy
- Publisher
- Wiley
- Published
- 2005-12-19
- Volume
- 9
- Issue
- 1-2
- Pages
- 95-108
- ISSN
- 1095-4244 (ISSN-L)
- 1099-1824
- OpenAlex
- W2141838814
- Cited by
- 261
- References
- 0
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nielsen, H. A., Madsen, H., & Nielsen, T. S. (2006). Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts. Wind Energy. https://doi.org/10.1002/we.180
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