HAJJAR, MHD KHAIRÜLKÜ, İLAYDA2023-04-042023-04-042022M. K. Hajjar and I. Ulku, "Forecasting of Turkey's Total Electricity Consumption in Sectoral Bases Using Machine Learning Algorithms," 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 2022, pp. 1-7, doi: 10.1109/HORA55278.2022.9799866.978-166546835-0https://doi.org/10.1109/HORA55278.2022.9799866https://hdl.handle.net/11413/8424Electrical energy is a milestone in the economic growth of each country. This study forecasts the sectoral and total electricity consumption in Turkey until the year 2050. This study, utilize two distinct time series forecasting methods namely Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO) as a model to generate the forecasting formulas. The sectoral and total electricity consumption for Turkey from the year 1970 to 2020 was obtained from the Turkish Statistical Institute and fed to the models to forecast the upcoming years. The two models were evaluated and compared using determination coefficient R2 and mean absolute percentage error MAPE. It is found that MLP performed better in forecasting the commercial, governmental, illumination and other sectors and SMO performed better in forecasting the industrial and household sectors alongside the total electricity consumption. © 2022 IEEE.eninfo:eu-repo/semantics/restrictedAccessArtificial Neural NetworkForecasting Electricity ConsumptionMachine LearningMultilayer PerceptronSMO RegressionWEKAconferenceObject2-s2.0-85133976741