Hatira, N.Alsan, H.F.Arsan, T.2025-01-152025-01-1520240979-835037943-3https://doi.org/10.1109/ASYU62119.2024.10757109https://hdl.handle.net/20.500.12469/7138IEEE SMC; IEEE Turkiye SectionAs global demand for energy continues to rise, understanding the trends and dynamics of energy generation is crucial to ensure a sustainable and efficient energy future. This study employs data science techniques to analyze global energy production data from 48 countries spanning 2010 to 2023. Initially, we use clustering methods to categorize countries based on their energy production profiles into three distinct groups: high, medium, and low production. This clustering provides insights into the diverse energy strategies and capacities across different regions. Subsequently, we apply and compare two classification models, specifically Random Forest and Gradient Boosting, to predict the dominant energy source for each cluster. Furthermore, we perform a comparative analysis of two forecasting models, SARIMA and Prophet, to predict future renewable energy production for countries with high production profiles, such as the USA and China. The forecasting results show the efficacy of these models in capturing seasonal trends and providing accurate predictions. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessClassificationClusteringData ScienceElectricity ProductionForecastingMachine LearningProphetRenewable EnergySarimaConference Object10.1109/ASYU62119.2024.107571092-s2.0-85213316363N/AN/A