A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods

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dc.contributor.authorCevik, Hasan Huseyin
dc.contributor.authorCunkas, Mehmet
dc.contributor.authorPolat, Kemal
dc.date.accessioned2020-03-26T20:12:20Z
dc.date.available2020-03-26T20:12:20Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, a new forecast model consist of three stages is proposed for the next hour wind power. In the first stage, wind speed, wind direction, and wind power have been forecasted by using historical data. Artificial Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Support Vector Regression (SVR) have been chosen as forecast methods, while Empirical Mode Decomposition (EMD) and Stationary Wavelet Decomposition (SWD) methods have been preferred as pre-processing methods. The other two stages have been used to improve the wind power forecast value obtained at the end of the first stage. In the second stage, the forecast values found in the first stage have been applied to the same forecast methods, and wind power forecast value has been updated. In the third stage, a correction process is applied, and the final forecast value is obtained. While four-year data are selected as train data, two-year data are tested. SWD-ANFIS has given the best results in the first stage while ANN has given the best result in the second stage. Finally, the ensemble result has been found by taking the weighted average of the results of the three methods. Mean Absolute Error (MAE) values found at each stage are the 0.333, 0.294 and 0.278, respectively. The obtained results have been compared with literature studies. The results show that the proposed multistage forecast model is capable of wind power forecasting efficiently and produce very close values to the actual data. (C) 2019 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.physa.2019.122177en_US
dc.identifier.issn0378-4371en_US
dc.identifier.issn1873-2119en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.physa.2019.122177
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37424
dc.identifier.volume534en_US
dc.identifier.wosWOS:000496334300073en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofPHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial Neuro-Fuzzy Inference Systemen_US
dc.subjectEmpirical Mode Decompositionen_US
dc.subjectShort-term wind power forecasten_US
dc.subjectStationary Wavelet Decompositionen_US
dc.subjectSupport Vector Regressionen_US
dc.titleA new multistage short-term wind power forecast model using decomposition and artificial intelligence methodsen_US
dc.typeArticleen_US

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