Forecasting Electricity Generation of a Geothermal Power Plant Using LSTM and GRU Networks

Basit Öğe Kaydı
dc.contributor.authorZor, Kasim
dc.contributor.authorTolun, Gulizar Gizem
dc.contributor.authorZor, Emine Seker
dc.date.accessioned2026-02-27T07:33:14Z
dc.date.available2026-02-27T07:33:14Z
dc.date.issued2025
dc.description7th Global Power Energy and Communication Conference-GPECOM-Annual
dc.description.abstractThe growing concerns over environmental variations and the depletion of fossil fuel reserves have encouraged many countries to prioritise on renewable energy sources. Geothermal power provides a clean and consistent energy source which enhances the diversification of energy supply profiles. However, accurate forecasting of geothermal electricity generation remains a challenging task due to the complex and dynamic characteristics of underground heat reservoirs. This study utilises machine learning (ML)-based methods to forecast an hour-ahead energy generation of the Kizildere 3 Geothermal Power Plant (GPP) which holds the largest installed capacity in Turkiye. ML-based algorithms present a robust alternative to conventional numerical modelling approaches by capturing non-linear relationships and ascending accuracy of forecast. By employing historical operational data, long short-term memory (LSTM) and gated recurrent unit (GRU) networks are evaluated and compared in terms of prediction performance metrics such as coefficient of determination (R-2) and normalised root mean squared error (nRMSE). The results highlight the potential of data-driven models in improving short-term geothermal energy forecasting which contributes to more efficient grid integration and operational planning.
dc.identifier.doi10.1109/GPECOM65896.2025.11061839
dc.identifier.endpage536
dc.identifier.isbn979-8-3315-1324-5; 979-8-3315-1323-8
dc.identifier.issn2832-7667
dc.identifier.startpage531
dc.identifier.urihttp://dx.doi.org/10.1109/GPECOM65896.2025.11061839
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4503
dc.identifier.wosWOS:001543723900090
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2025 7th Global Power, Energy and Communication Conference, Gpecom
dc.relation.ispartofseriesGlobal Power Energy and Communication Conference
dc.relation.publicationcategoryKonferans ��esi - Uluslararas� - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectGeothermal power
dc.subjectShort-term
dc.titleForecasting Electricity Generation of a Geothermal Power Plant Using LSTM and GRU Networks
dc.typeProceedings Paper

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu