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dc.contributor.authorGoretti, Gen_NZ
dc.contributor.authorDuffy, Aen_NZ
dc.contributor.authorLie, Ten_NZ
dc.date.accessioned2019-02-28T04:09:24Z
dc.date.available2019-02-28T04:09:24Z
dc.date.copyright2017-06-09en_NZ
dc.identifier.citation2017 14th International Conference on the European Energy Market (EEM), Dresden, 2017, pp. 1-4. doi: 10.1109/EEM.2017.7981885
dc.identifier.urihttp://hdl.handle.net/10292/12297
dc.description.abstractAn increasing number of utilities participating in the energy market require short term (i.e. up to 48 hours) power forecasts for renewable generation in order to optimize technical and financial performances. As a result, a large number of forecast providers now operate in the marketplace, each using different methods and offering a wide range of services. This paper assesses five different day-ahead wind power forecasts generated by various service providers currently operating in the market, and compares their performance against the state-of-the-art of short-term wind power forecasting. The work focuses on how power curve estimations can introduce systematic errors that affect overall forecast performance. The results of the study highlight the importance of: accurately modelling the wind speed-to-power output relationships at higher wind speeds; taking account of power curve trends when training models; and the need to incorporate long-term (months to years) power curve variability into the forecast updating process.
dc.publisherIEEE
dc.relation.urihttps://ieeexplore.ieee.org/document/7981885
dc.rightsCopyright © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectWind power forecast; Wind turbine power curve; Short-term forecasting; Forecast assessment; Wind energy
dc.titleThe Impact of Power Curve Estimation on Commercial Wind Power Forecasts: An Empirical Analysisen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.1109/EEM.2017.7981885
pubs.elements-id283983
aut.relation.conference14th International Conference on the European Energy Marketen_NZ


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