Current status of wind energy forecasting and a hybrid method for hourly predictions

dc.contributor.authorOkumus, Inci
dc.contributor.authorDinler, Ali
dc.date.accessioned2025-05-10T19:49:22Z
dc.date.issued2016
dc.departmentİstanbul Medeniyet Üniversitesi
dc.description.abstractGenerating accurate wind energy and/or power forecasts is crucially important for energy trading and planning. The present study initially gives an extensive review of recent advances in statistical wind forecasting. Numerous prediction methods for varying prediction horizons from a few seconds to several months are listed. Then in the light of accurate results in the literature, the present study combines the adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) for 1 h ahead wind speed forecasts. The performance results show the mean absolute percentage errors (MAPE) of 2.2598%, 3.3530% and 3.8589% at three different locations for daily average wind speeds. (C) 2016 Elsevier Ltd. All rights reserved.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [213M549]; Scientific Research Projects Program of Istanbul Medeniyet University [FBA-2013-412]
dc.description.sponsorshipThe first author (I.O.) is partly supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with a grant number of 213M549. The authors would also like to acknowledge the fund from the Scientific Research Projects Program of Istanbul Medeniyet University with a project number FBA-2013-412 for computing resources.
dc.identifier.doi10.1016/j.enconman.2016.06.053
dc.identifier.endpage371
dc.identifier.issn0196-8904
dc.identifier.issn1879-2227
dc.identifier.scopus2-s2.0-84975869372
dc.identifier.scopusqualityQ1
dc.identifier.startpage362
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2016.06.053
dc.identifier.urihttps://hdl.handle.net/20.500.14730/11986
dc.identifier.volume123
dc.identifier.wosWOS:000380601300031
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEnergy Conversion and Management
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250302
dc.subjectWind energy
dc.subjectWind power
dc.subjectWind energy forecasting
dc.titleCurrent status of wind energy forecasting and a hybrid method for hourly predictions
dc.typeReview

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