An improved residual-based convolutional neural network for very short-term wind power forecasting

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dc.authorid0000-0002-5159-0659en_US
dc.contributor.authorYıldız, Ceyhun
dc.contributor.authorAçıkgöz, Hakan
dc.contributor.authorKorkmaz, Deniz
dc.contributor.authorBudak, Ümit
dc.date.accessioned2022-03-22T13:18:06Z
dc.date.available2022-03-22T13:18:06Z
dc.date.issued2021en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAn accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy producers, exposing them to potential additional costs. Being aware of this challenge, we introduced a two-step novel deep learning method for wind power forecasting. The first stage includes processes of Variational Mode Decomposition (VMD)-based feature extraction and converting these features into images. In the second stage, an improved residual-based deep Convolutional Neural Network (CNN) was utilized to forecast wind power. Meteorological wind speed, wind direction, and wind power data, which are directly related to each other, were employed as a dataset. The combined dataset was procured from a wind farm in Turkey between January 1 and December 31, 2018. The results of the proposed method were compared with the results obtained from the state-of-the-art deep learning architectures namely SqueezeNet, GoogLeNet, ResNet-18, AlexNet, and VGG-16 as well as physical model based on available meteorological forecast data. The proposed method outperformed the other architectures and demonstrated promising results for very short-term wind power forecasting due to its competitive performance.en_US
dc.identifier.citationYildiz, C., Acikgoz, H., Korkmaz, D., & Budak, U. (2021). An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Conversion and Management, 228, 113731.en_US
dc.identifier.doi10.1016/j.enconman.2020.113731
dc.identifier.issue113731en_US
dc.identifier.scopus2-s2.0-85097636813en_US
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2020.113731
dc.identifier.urihttps://hdl.handle.net/20.500.12899/798
dc.identifier.volume228en_US
dc.identifier.wosWOS:000607501700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKorkmaz, Deniz
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.echttps://doi.org/10.1016/j.enconman.2020.113731
dc.relation.echttps://doi.org/10.1016/j.enconman.2020.113731
dc.relation.ispartofEnergy Conversion and Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWind power forecastingen_US
dc.subjectResidual networken_US
dc.subjectConvolutional neural networken_US
dc.subjectVariational mode decompositionen_US
dc.subjectDeep learningen_US
dc.titleAn improved residual-based convolutional neural network for very short-term wind power forecastingen_US
dc.typeArticleen_US

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