A DEEP LEARNING NETWORK FOR MODELLING THE RELATIONSHIP OF THE REAL GDP, CO2 EMISSION AND RENEWABLE ENERGY CONSUMPTION FOR TURKIYE

Abstract

In this study, the relationship between Türkiye’s real gross domestic product, CO2 emission and renewable energy consumption is modelled using machine learning techniques. The data between 2003-2020, the period when renewable energy production and consumption have accelerated, are included in the modelling. First of all, it was tested whether there is a causal relationship between the real gross domestic product, CO2 emission and renewable energy consumption data. Afterwards, a deep learning model was developed in Python programming language by considering the real gross domestic product as the dependent variable while CO2 emission and renewable energy consumption are considered as independent variables. The developed deep learning model has two input nodes, three hidden layers consisting of 100 neurons each, and an output node. In addition, the rectified unit functions are used as nonlinear activation functions in the deep learning network. Based on the standard usage, seventy percent of the data was used as the training data and the remaining thirty percent were employed as the test data. The results of the developed deep learning network and actual gross domestic product data were compared, and it is shown that the developed deep learning network successfully models the relationship between the real gross domestic product, CO2 emission and the renewable energy consumption. The coefficient of determination of the developed model was calculated in Python environment as R2=0.986. This parameter value also indicates that the developed deep learning network model has a good performance for the modelling of the real gross domestic product dependent on the CO2 emission and renewable energy consumption.