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Journal article

Probabilistic forecasting of wind power ramp events using autoregressive logit models

Abstract:: A challenge for the efficient operation of power systems and wind farms is the occurrence of wind power ramps, which are sudden large changes in the power output from a wind farm. This paper considers the probabilistic forecasting of a ramp event, defined as exceedance beyond a specified threshold. We directly model the exceedance probability using autoregressive logit models fitted to the change in wind power. These models can be estimated by maximising a Bernoulli likelihood. We introduce a...
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Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Taylor, J. (2016). Probabilistic forecasting of wind power ramp events using autoregressive logit models. European Journal of Operational Research, 259(2), 703–712.

MLA Style

Taylor, J. “Probabilistic Forecasting of Wind Power Ramp Events Using Autoregressive Logit Models.” European Journal of Operational Research, vol. 259, no. 2, Elsevier, 2016, pp. 703–12.

Chicago Style

Taylor, J. 2016. “Probabilistic Forecasting of Wind Power Ramp Events Using Autoregressive Logit Models.” European Journal of Operational Research 259 (2): 703–12.
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Files:: Probabilistic forecast...

(Accepted manuscript, pdf, 1.3MB)

Publisher copy:: 10.1016/j.ejor.2016.10.041

Authors

+ Taylor, J More by this author

Institution:

University of Oxford

Division:

SSD

Department:

Said Business School

Role:

Author

Bibliographic Details

Publisher:: Elsevier Publisher's website
Journal:: European Journal of Operational Research Journal website
Volume:: 259
Issue:: 2
Pages:: 703–712
Publication date:: 2016-10-24
Acceptance date:: 2016-10-20
DOI:: 10.1016/j.ejor.2016.10.041
EISSN:: 1872-6860
ISSN:: 0377-2217

Item Description

Keywords:: autoregressive logit models

OR in energy

probability forecasting

wind power ramps
Pubs id:: pubs:656537
UUID:: uuid:ca990b8d-a847-4696-9bb8-774f824448ee
Local pid:: pubs:656537
Source identifiers:: 656537
Deposit date:: 2016-11-02

Terms of use

Copyright holder:: Elsevier BV
Copyright date:: 2016
Notes:: Copyright © 2016 Elsevier B.V. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.ejor.2016.10.041

Licence:: CC Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND)

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