Erdem, O.E.Sarucan, A.Baysal, M.E.2023-11-112023-11-11202397830313977692367-3370https://doi.org/10.1007/978-3-031-39777-6_21https://hdl.handle.net/20.500.13091/4765Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference -- 22 August 2023 through 24 August 2023 -- -- 299549This study used statistical data to create predictive models for effective and efficient use of energy generation systems using machine learning algorithms. Four different models predicted wind energy production and compared with actual production data. Model validity and verification were evaluated using mean square error, mean absolute error, and R-squared values. The model constructed with the random forest method is the most successful model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessDataEnergyMachine LearningForestryLearning algorithmsMean square errorWind powerWind turbinesDataEfficient use of energyEnergyEnergy generationsEnergy productionsGeneration systemsMachine learning algorithmsMachine-learningPredictive modelsStatistical datasMachine learningConference Object10.1007/978-3-031-39777-6_212-s2.0-85172732344