Daily solar radiation prediction using LSTM neural networks

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dc.contributor.authorGider, Veysel
dc.contributor.authorBudak, Cafer
dc.contributor.authorİzci, Davut
dc.contributor.authorEkinci, Serdar
dc.date.accessioned2024-04-24T17:56:25Z
dc.date.available2024-04-24T17:56:25Z
dc.date.issued2022
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elktronik 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.abstractThe integration of solar energy with the smart grids and existing infrastructure makes it a cost-effective and environmentally-friendly solution to address the growing energy need. To make use of the potential of solar energy, several challenges such as the stability of generated energy and the supply-demand imbalance must be overcome. In this regard, an accurate forecast model for global solar radiation (GSR) can be useful for power generation planning and system reliability. The GSR estimate is regarded as the most significant and critical element in defining solar system characteristics, thus, it is crucial in predicting the generated energy. This work, therefore, employs long-short-term memory (LSTM) as a deep learning method to successfully estimate solar irradiance and capture the stochastic fluctuations. In this respect, the measurement data (from year 2021) obtained from the station installed in Dicle University (Turkey), Science and Technology Application and Research Centre (DUBTAM) were used, and the efficiency of the proposed method was evaluated. © 2022 IEEE.en_US
dc.identifier.citationGider, V., Budak, C., İzci, D. ve Ekinci, S. (2022). Daily solar radiation prediction using LSTM neural networks. IEEE Global Energy Conference, GEC 2022, 168-172.
dc.identifier.doi10.1109/GEC55014.2022.9987055
dc.identifier.endpage172en_US
dc.identifier.isbn9781665497510
dc.identifier.scopus2-s2.0-85146489146
dc.identifier.scopusqualityN/A
dc.identifier.startpage168en_US
dc.identifier.urihttps://doi.org/10.1109/GEC55014.2022.9987055
dc.identifier.urihttps://hdl.handle.net/11468/23501
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.subjectArtificial neural networksen_US
dc.subjectDeep learningen_US
dc.subjectLstmen_US
dc.subjectRadiation predictionen_US
dc.subjectSolar energyen_US
dc.titleDaily solar radiation prediction using LSTM neural networksen_US
dc.titleDaily solar radiation prediction using LSTM neural networks
dc.typeConference Objecten_US

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