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Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique
The objectives of the study are twofold. First, we aim to examine the most influential socio-economic indicators to explain energy consumption in Republic of Serbia. The second objective is to develop models that are able to predict the future energy consumption in the Republic of Serbia. This could be the first important step towards proper energy management in the country. Several potential socio-economic indicators are selected to be the independent variables. Regression analysis is conducted to select the most relevant independent variables as well as building the multiple linear regression (MLR) models. In addition, an artificial neural networks (ANN) model is developed as a comparison. Finally, the energy demand is projected to the year 2022. It is found that both models show the declining trend with respect to the current level of energy consumption.
Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique
The objectives of the study are twofold. First, we aim to examine the most influential socio-economic indicators to explain energy consumption in Republic of Serbia. The second objective is to develop models that are able to predict the future energy consumption in the Republic of Serbia. This could be the first important step towards proper energy management in the country. Several potential socio-economic indicators are selected to be the independent variables. Regression analysis is conducted to select the most relevant independent variables as well as building the multiple linear regression (MLR) models. In addition, an artificial neural networks (ANN) model is developed as a comparison. Finally, the energy demand is projected to the year 2022. It is found that both models show the declining trend with respect to the current level of energy consumption.
Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique
Protić, Milan (author) / Fathurrahman, Fahman (author) / Raos, Miomir (author)
2019-01-01
Tehnički vjesnik ; ISSN 1330-3651 (Print) ; ISSN 1848-6339 (Online) ; Volume 26 ; Issue 1
Article (Journal)
Electronic Resource
English
DDC:
690
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