A Short-Term Load Demand Forecasting based on the Method of LSTM

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dc.authoridÖztürk, Nihat/0000-0002-0607-1868
dc.authorwosidÖztürk, Nihat/H-8697-2018
dc.contributor.authorBodur, İdris
dc.contributor.authorÇelik, Emre
dc.contributor.authorÖztürk, Nihat
dc.date.accessioned2023-07-26T11:57:29Z
dc.date.available2023-07-26T11:57:29Z
dc.date.issued2021
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description10th IEEE International Conference on Renewable Energy Research and Applications (ICRERA) -- SEP 26-29, 2021 -- Istanbul, TURKEYen_US
dc.description.abstractElectricity energy is produced from another energy source like fossil source such as oil, coil, natural gas renewable energy sources such as hydraulic, wind, solar. Their storage in high amount is a problematic issue. Therefore, the balance between the power generation and demanded power must be satisfied at all times. This is an obligation especially for companies that generate, transmit and distribute electrical energy. In this paper, a short term load demand forecasting based on a long short term memory (LSTM) is addressed, which may help planning operators for Turkish electricity market. The results of advocated approach were compared by the ones based on recurrent neural network As a result, it is found that the proposed LSTM approach can predict especially daily and weekly demands with an accuracy more than 90%.en_US
dc.description.sponsorshipIEEE,IjSmartGrid,IcSmartGrid,TMEIC,Honda R & D Co Ltd,Nisantasi Univ,Int Journal Renewable Energy Res,Nagasaki Univ,Nagasaki Inst Appl Sci,Gazi Univ,IEEE Ind Applicat Soc,IESen_US
dc.identifier.doi10.1109/ICRERA52334.2021.9598773
dc.identifier.endpage174en_US
dc.identifier.isbn978-1-6654-4524-5
dc.identifier.issn2377-6897
dc.identifier.scopus2-s2.0-85123168844en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage171en_US
dc.identifier.urihttps://doi.org/10.1109/ICRERA52334.2021.9598773
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13197
dc.identifier.wosWOS:000761616700027en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇelik, Emre
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof10th Ieee International Conference on Renewable Energy Research and Applications (Icrera 2021)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectShort Term Load Forecasting; Long-Short-Term Memory; Recurrent Neural Networken_US
dc.titleA Short-Term Load Demand Forecasting based on the Method of LSTMen_US
dc.typeConference Objecten_US

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