Şentürk, ÜmitBeken, MuratEyecioğlu, Önder2024-09-252024-09-252022978-166547140-4https://doi.org/10.1109/ICRERA55966.2022.9922702https://hdl.handle.net/20.500.12491/12301Istanbul Nisantasi University; NTEC; TMEiC11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 -- 18 September 2022 through 21 September 2022 -- Istanbul -- 183802In today's energy crisis, countries need to know their energy consumption and make their energy investments accordingly. The variability of end users demanding energy makes it difficult to estimate energy needs. In this article, it has been tried to forecast the future consumption from the electrical energy data consumed in Turkey between the years 2016-2022. After the electricity consumption data was converted into daily data, electrical energy consumption estimations were made with machine learning methods such as linear regression, tree regression, voting regression, XGB regression and Artificial neural network (ANN) methods. Estimation results were evaluated with Mean Square Error (MSE) and R2 (coefficient of determination) performance metrics. As a result of the evaluations made with the test data, MSE=0.006 (0-1 min-max normalization dataset) and R2= 82.7 performances, voting regression obtained the best result among the methods used. Accurate estimation of energy consumption will enable energy production to be made at the optimum level. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessEnsamble Voting Regressor ANNPower Consumption ForecastTree RegressorXGB RegressorConference Object10.1109/ICRERA55966.2022.99227026016052-s2.0-85142076163N/A