Zor, KasimTolun, Gulizar GizemZor, Emine Seker2026-02-272026-02-272025979-8-3315-1324-5; 979-8-3315-1323-82832-766710.1109/GPECOM65896.2025.11061839http://dx.doi.org/10.1109/GPECOM65896.2025.11061839https://hdl.handle.net/20.500.14669/45037th Global Power Energy and Communication Conference-GPECOM-AnnualThe 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.eninfo:eu-repo/semantics/closedAccessForecastingMachine learningGeothermal powerShort-termProceedings Paper536531WOS:001543723900090