Energy Demand and CO2 Emission Forecast Model for Turkey with Deep Learning and Machine Learning Algorithms - Bolat, Emre; Arikan Yildiz, Yagmur (Elektronika ir Elektrotechnika, 2025)
This study has conducted a forecast analysis of the energy demand and carbon dioxide (CO2) emissions of Turkey, a developing country. Considering Turkey’s rapidly increasing energy demand, various economic and social parameters have been used for the years 1990-2024. Both machine learning and deep learning methods have been applied, and artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and linear regression (LR) algorithms have been used for two models. The performance of these models has been assessed using various error metrics. The ANN has demonstrated the highest accuracy in modelling energy demand, achieving a coefficient of determination of 98.89 %, while the RNN has shown the best performance in modelling CO2 emissions, with a coefficient of determination of 96.80 %. The findings have shown that the growth rates in energy demand and CO2 emissions are high in the early years but slowed in the following years. However, it has been determined that the general trend continued to increase. The study emphasises the need for Turkey to diversify its energy sources and increase the use of renewable energy to meet its increasing energy demand. It also has concluded that accelerating efforts to achieve net zero emission targets are critical to long-term energy security and environmental sustainability.