A Univariate Time Series Methodology Based on Sequence-To Learning for Short To Midterm Wind Power Production

dc.authorid Ünlü, Kamil Demirberk/0000-0002-2393-6691
dc.authorscopusid 56543736000
dc.authorscopusid 57210105250
dc.authorwosid Akbal, Yıldırım/ITT-5282-2023
dc.authorwosid Ünlü, Kamil Demirberk/AAL-5952-2020
dc.contributor.author Akbal, Yildirim
dc.contributor.author Unlu, Kamil Demirberk
dc.contributor.other Industrial Engineering
dc.contributor.other Mathematics
dc.date.accessioned 2024-07-05T15:24:05Z
dc.date.available 2024-07-05T15:24:05Z
dc.date.issued 2022
dc.department Atılım University en_US
dc.department-temp [Akbal, Yildirim] TED Univ, Grad Program Appl Data Sci, TR-06420 Ankara, Turkey; [Unlu, Kamil Demirberk] Atilim Univ, Dept Ind Engn, TR-06830 Ankara, Turkey en_US
dc.description Ünlü, Kamil Demirberk/0000-0002-2393-6691 en_US
dc.description.abstract The biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a nonstorable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R-2) in short term and moderate R-2 in the midterm forecast. Although in the midrange forecasts R-2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas. en_US
dc.identifier.citationcount 14
dc.identifier.doi 10.1016/j.renene.2022.10.055
dc.identifier.endpage 844 en_US
dc.identifier.issn 0960-1481
dc.identifier.issn 1879-0682
dc.identifier.scopus 2-s2.0-85140145050
dc.identifier.startpage 832 en_US
dc.identifier.uri https://doi.org/10.1016/j.renene.2022.10.055
dc.identifier.uri https://hdl.handle.net/20.500.14411/2386
dc.identifier.volume 200 en_US
dc.identifier.wos WOS:000889557400003
dc.identifier.wosquality Q1
dc.institutionauthor Ünlü, Kamil Demirberk
dc.institutionauthor Akbal, Yıldırım
dc.language.iso en en_US
dc.publisher Pergamon-elsevier Science Ltd en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 24
dc.subject LSTM en_US
dc.subject GRU en_US
dc.subject Turkey en_US
dc.subject Wind power en_US
dc.subject Electricity production en_US
dc.subject Time series analysis en_US
dc.title A Univariate Time Series Methodology Based on Sequence-To Learning for Short To Midterm Wind Power Production en_US
dc.type Article en_US
dc.wos.citedbyCount 19
dspace.entity.type Publication
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