Kuskapan, EmreCodur, Muhammed YasinCodur, Merve KayaciDissanayake, Dilum2026-03-262026-03-2620262050-050510.1002/ese3.704262-s2.0-105026182200https://doi.org/10.1002/ese3.70426https://hdl.handle.net/20.500.14901/2831Kuşkapan, Emre/0000-0003-0711-5567; Kayacı Çodur, Merve/0000-0003-1459-9678; Dissanayake, Dilum/0000-0001-6166-5709Predicting energy consumption helps countries make strategic decisions in many critical areas such as energy management, economic development, energy security, environmental sustainability and infrastructure investments. Therefore, accurate and reliable energy consumption predictions are vital to ensure the sustainability and prosperity of countries. This study aims to contribute to the proper planning of transportation policies and energy management by successfully predicting T & uuml;rkiye's railway energy consumption. In this direction, energy prediction values were obtained from 18 different machine learning methods using the country's railway line length, number of passengers, freight amount and energy consumption values from 1977 to 2024. To further strengthen the results obtained with these methods, bagging, boosting, stacking and blending ensemble learning methods were utilized. With the improvements, the R-squared value was increased up to 0.9667 and energy predicting was achieved with very high accuracy. Based on the results obtained from this study, it is possible to provide investment planning more efficiently. In addition, the implementation of energy management strategies, infrastructure planning and sustainable energy policies will be provided more efficiently as a result of obtaining more successful results by using ensemble machine learning methods instead of traditional machine learning methods for energy consumption predictions in different sectors.eninfo:eu-repo/semantics/openAccessEnergy DemandEnsemble Machine LearningRailway TransportationSustainabilityArticle