Electrical energy consumption forecasting using regression method considering temperature effect for distribution network

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dc.authoridYILDIRIZ, Gulsum/0000-0001-6075-7310
dc.contributor.authorYıldırız, Gülsüm
dc.contributor.authorÖztürk, Ali
dc.date.accessioned2023-07-26T11:58:44Z
dc.date.available2023-07-26T11:58:44Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractLoad profile coefficients (LPCs) represent the pattern of electricity usage daily and yearly for electrical energy consumers. It is important to determine the LPCs accurately and reliably, in order to minimize the imbalance costs in the Electricity Energy Market. Reliable methods and sufficient measurement data are required to make accurate forecasts. The local distribution company (TLDC) already calculates the profile coefficients by taking the average of the consumptions without meteorological measurements in Turkey. TLDC determines the LPC by receiving hourly consumption data directly from the consumers. In this paper, the mathematical forecasting models (MFMs) have been produced for determining LPC Duzce in Turkey using the multiple regression analysis method for the first time. Firstly, hourly electrical energy consumption and meteorological temperatures were measured in some predetermined residential subscribers. The MFMs have been produced by using the measured data, and then, LPCs have been determined by using the MFMs. The electrical energy consumptions have been estimated using the determined LPCs, and the estimation results have been compared with the measurement data. The MFMs have been subjected to suitability tests accepted in the literature, and the performances of the models have been verified. According to the results obtained, it has been seen that the MFMs can estimate loads with an accuracy of up to 96% depending on the future changing meteorological conditions, and it has been proposed as a quick and practical method for LPCs calculation. The paper shows that the produced MFMs provide obtaining satisfactory results for energy consumption forecasting for Duzce in Turkey.en_US
dc.identifier.doi10.1007/s00202-022-01559-8
dc.identifier.endpage3476en_US
dc.identifier.issn0948-7921
dc.identifier.issn1432-0487
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85129302465en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage3465en_US
dc.identifier.urihttps://doi.org/10.1007/s00202-022-01559-8
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13556
dc.identifier.volume104en_US
dc.identifier.wosWOS:000790159500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYıldırız, Gülsüm
dc.institutionauthorÖztürk, Ali
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofElectrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectTime Series Method; Load Forecasting; Multiple Regression Analysisen_US
dc.subjectSupport Vector Regression; Quantile Regression; Neural-Network; Load; Model; Arimaen_US
dc.titleElectrical energy consumption forecasting using regression method considering temperature effect for distribution networken_US
dc.typeArticleen_US

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