MACHINE LEARNING IN ENERGY EFFICIENCY: COMPARISON OF ENERGY ESTIMATION MODELS

Authors : Muhammed Akif Yenikaya, Onur Oktaysoy

Pages : 196-210

Doi:10.17753/sosekev.1636999

View : 60 | Download : 68

Publication Date : 2025-09-10

Article Type : Research Paper

Abstract :In this study, the functionality of machine learning models was tried to be determined in order to estimate the energy consumption needed in industrial production. In this context, Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors and Support Vector Machine algorithms were compared and evaluated in terms of usability and performance values. In the study, five different machine learning models were compared to estimate energy consumption. In order to make energy consumption estimates, historical production data, energy consumption data and other relevant parameters were used as input data. Data was obtained from UCI data repository, an open-source platform. The machine learning process structured as 80/20 training/testing was adapted to the form where the models can perform energy efficiency analysis with comprehensive data parameters. Error metrics such as coefficient of determination (R²), root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) were used to evaluate the performance of the models. According to the findings, the Random Forest model used in the study provided a higher accuracy rate compared to other models, and the R² value was obtained as 0.9989. This result reveals that machine learning models can be used as effective tools in estimating energy consumption and that these tools can turn into a strategic advantage for businesses, considering the importance of energy in production. The research provides significant contributions to literature by revealing that machine learning technology can be an important tool in energy consumption and, moreover, by comparing the performance of different models in energy estimation.
Keywords : Enerji tüketimi, Kaynak verimliliği, Makine öğrenmesi, Enerji tahmini, Teknoloji

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