Electricity consumption forecast using machine learning regression models in Turkey

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dc.authorid0000-0001-9610-9550
dc.authorid0000-0003-2989-3781
dc.authorid0000-0002-9735-5697
dc.authorscopusid57203169526
dc.authorscopusid35486816200
dc.authorscopusid18037100700
dc.contributor.authorŞentürk, Ümit
dc.contributor.authorBeken, Murat
dc.contributor.authorEyecioğlu, Önder
dc.date.accessioned2024-09-25T19:42:51Z
dc.date.available2024-09-25T19:42:51Z
dc.date.issued2022
dc.departmentBAİBÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionIstanbul Nisantasi University; NTEC; TMEiCen_US
dc.description11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 -- 18 September 2022 through 21 September 2022 -- Istanbul -- 183802en_US
dc.description.abstractIn today's energy crisis, countries need to know their energy consumption and make their energy investments accordingly. The variability of end users demanding energy makes it difficult to estimate energy needs. In this article, it has been tried to forecast the future consumption from the electrical energy data consumed in Turkey between the years 2016-2022. After the electricity consumption data was converted into daily data, electrical energy consumption estimations were made with machine learning methods such as linear regression, tree regression, voting regression, XGB regression and Artificial neural network (ANN) methods. Estimation results were evaluated with Mean Square Error (MSE) and R2 (coefficient of determination) performance metrics. As a result of the evaluations made with the test data, MSE=0.006 (0-1 min-max normalization dataset) and R2= 82.7 performances, voting regression obtained the best result among the methods used. Accurate estimation of energy consumption will enable energy production to be made at the optimum level. © 2022 IEEE.en_US
dc.identifier.doi10.1109/ICRERA55966.2022.9922702
dc.identifier.endpage605en_US
dc.identifier.isbn978-166547140-4
dc.identifier.scopus2-s2.0-85142076163en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage601en_US
dc.identifier.urihttps://doi.org/10.1109/ICRERA55966.2022.9922702
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12301
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞentürk, Ümit
dc.institutionauthorBeken, Murat
dc.institutionauthorEyecioğlu, Önder
dc.institutionauthorid0000-0001-9610-9550
dc.institutionauthorid0000-0003-2989-3781
dc.institutionauthorid0000-0002-9735-5697
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectEnsamble Voting Regressor ANNen_US
dc.subjectPower Consumption Forecasten_US
dc.subjectTree Regressoren_US
dc.subjectXGB Regressoren_US
dc.titleElectricity consumption forecast using machine learning regression models in Turkeyen_US
dc.typeConference Objecten_US

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