The electricity price prediction of victoria city based on various regression algorithms
dc.contributor.author | Örenç, Sedat | |
dc.contributor.author | Acar, Emrullah | |
dc.contributor.author | Özerdem, Mehmet Siraç | |
dc.date.accessioned | 2024-04-24T17:56:24Z | |
dc.date.available | 2024-04-24T17:56:24Z | |
dc.date.issued | 2022 | |
dc.department | Dicle Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.description | Batman University and Batman Energy Coordination Center (EKOM) | en_US |
dc.description | 2022 IEEE Global Energy Conference, GEC 2022 -- 26 October 2022 through 29 October 2022 -- -- 185674 | en_US |
dc.description.abstract | Precise 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.citation | Örenç, 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.doi | 10.1109/GEC55014.2022.9986605 | |
dc.identifier.endpage | 167 | en_US |
dc.identifier.isbn | 9781665497510 | |
dc.identifier.scopus | 2-s2.0-85146497784 | |
dc.identifier.scopusquality | N/A | |
dc.identifier.startpage | 164 | en_US |
dc.identifier.uri | https://doi.org/10.1109/GEC55014.2022.9986605 | |
dc.identifier.uri | https://hdl.handle.net/11468/23499 | |
dc.indekslendigikaynak | Scopus | |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | IEEE Global Energy Conference, GEC 2022 | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Decision tree regression | en_US |
dc.subject | Electricity price prediction | en_US |
dc.subject | Gradient boosting regression | en_US |
dc.subject | Linear regression algorithm | en_US |
dc.subject | Random forest regression | en_US |
dc.title | The electricity price prediction of victoria city based on various regression algorithms | en_US |
dc.title | The electricity price prediction of victoria city based on various regression algorithms | |
dc.type | Conference Object | en_US |
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