Electricity consumption forecast using machine learning regression models in Turkey
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Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
Istanbul Nisantasi University; NTEC; TMEiC
11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 -- 18 September 2022 through 21 September 2022 -- Istanbul -- 183802
11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 -- 18 September 2022 through 21 September 2022 -- Istanbul -- 183802
Anahtar Kelimeler
Ensamble Voting Regressor ANN, Power Consumption Forecast, Tree Regressor, XGB Regressor
Kaynak
11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022
WoS Q Değeri
Scopus Q Değeri
N/A
