The electricity price prediction of victoria city based on various regression algorithms

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dc.contributor.authorÖrenç, Sedat
dc.contributor.authorAcar, Emrullah
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2024-04-24T17:56:24Z
dc.date.available2024-04-24T17:56:24Z
dc.date.issued2022
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.descriptionBatman University and Batman Energy Coordination Center (EKOM)en_US
dc.description2022 IEEE Global Energy Conference, GEC 2022 -- 26 October 2022 through 29 October 2022 -- -- 185674en_US
dc.description.abstractPrecise electricity price prediction is extremely important for all markets especially for families' life conditions because the more demand the more electricity price increases, therefore it is vital to keep the balance between demand and supply. It is crucial to know how much electricity is needed for the future as it has a remarkable impact on economic circumstances. This article proposes four productive methods in order to forecast high-precision results. In the regression algorithms, it is used several methods which are called decision tree regressions, random forest regression, gradient boosting regression, and linear regression algorithms. The dataset is divided into three parts. Training, validation, and test are split into %70, %10, and %20 respectively. The empirical and efficient results show that these methods can be used and reduce errors. The article demonstrates that a novel forecasting model can be designed for the future.en_US
dc.identifier.citationÖrenç, S., Acar, E. ve Özerdem, M. S. (2022). The electricity price prediction of victoria city based on various regression algorithms. IEEE Global Energy Conference, GEC 2022, 164-167.
dc.identifier.doi10.1109/GEC55014.2022.9986605
dc.identifier.endpage167en_US
dc.identifier.isbn9781665497510
dc.identifier.scopus2-s2.0-85146497784
dc.identifier.scopusqualityN/A
dc.identifier.startpage164en_US
dc.identifier.urihttps://doi.org/10.1109/GEC55014.2022.9986605
dc.identifier.urihttps://hdl.handle.net/11468/23499
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Global Energy Conference, GEC 2022
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision tree regressionen_US
dc.subjectElectricity price predictionen_US
dc.subjectGradient boosting regressionen_US
dc.subjectLinear regression algorithmen_US
dc.subjectRandom forest regressionen_US
dc.titleThe electricity price prediction of victoria city based on various regression algorithmsen_US
dc.titleThe electricity price prediction of victoria city based on various regression algorithms
dc.typeConference Objecten_US

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