Current status of wind energy forecasting and a hybrid method for hourly predictions
| dc.contributor.author | Okumus, Inci | |
| dc.contributor.author | Dinler, Ali | |
| dc.date.accessioned | 2025-05-10T19:49:22Z | |
| dc.date.issued | 2016 | |
| dc.department | İstanbul Medeniyet Üniversitesi | |
| dc.description.abstract | Generating 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.sponsorship | Scientific and Technological Research Council of Turkey (TUBITAK) [213M549]; Scientific Research Projects Program of Istanbul Medeniyet University [FBA-2013-412] | |
| dc.description.sponsorship | The 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.doi | 10.1016/j.enconman.2016.06.053 | |
| dc.identifier.endpage | 371 | |
| dc.identifier.issn | 0196-8904 | |
| dc.identifier.issn | 1879-2227 | |
| dc.identifier.scopus | 2-s2.0-84975869372 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 362 | |
| dc.identifier.uri | https://doi.org/10.1016/j.enconman.2016.06.053 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14730/11986 | |
| dc.identifier.volume | 123 | |
| dc.identifier.wos | WOS:000380601300031 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Pergamon-Elsevier Science Ltd | |
| dc.relation.ispartof | Energy Conversion and Management | |
| dc.relation.publicationcategory | Diğer | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_WOS_20250302 | |
| dc.subject | Wind energy | |
| dc.subject | Wind power | |
| dc.subject | Wind energy forecasting | |
| dc.title | Current status of wind energy forecasting and a hybrid method for hourly predictions | |
| dc.type | Review |
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