Comparing Multilayer Perceptron and Multiple Regression Models for Predicting Energy Use in the Balkans

TL;DR

This study compares the effectiveness of multilayer perceptron neural networks and multiple regression models in predicting energy consumption in Balkan countries, finding neural networks to be more accurate.

Contribution

It introduces a comparative analysis of neural network and regression models for energy prediction using Balkan data from 1995-2014.

Findings

MLP predicts energy consumption more accurately than regression.

CO2 emissions have the highest impact on energy consumption.

GDP has a significant but lesser impact than CO2 emissions.

Abstract

Global demographic and economic changes have a critical impact on the total energy consumption, which is why demographic and economic parameters have to be taken into account when making predictions about the energy consumption. This research is based on the application of a multiple linear regression model and a neural network model, in particular multilayer perceptron, for predicting the energy consumption. Data from five Balkan countries has been considered in the analysis for the period 1995-2014. Gross domestic product, total number of population, and CO2 emission were taken as predictor variables, while the energy consumption was used as the dependent variable. The analyses showed that CO2 emissions have the highest impact on the energy consumption, followed by the gross domestic product, while the population number has the lowest impact. The results from both analyses are then used for making predictions on the same data, after which the obtained values were compared with the real values. It was observed that the multilayer perceptron model predicts better the energy consumption than the regression model.