The New Prediction Methodology for CO 2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach

İnayet Özge Aksu () and Tuğçe Demirdelen
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İnayet Özge Aksu: Department of Computer Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Türkiye
Tuğçe Demirdelen: Department of Electrical and Electronics Engineering, Adana Alparslan Turkes Science and Technology University, 01250 Adana, Türkiye

Sustainability, 2022, vol. 14, issue 23, 1-29

Abstract: Energy is one of the most fundamental elements of today’s economy. It is becoming more important day by day with technological developments. In order to plan the energy policies of the countries and to prevent the climate change crisis, CO 2 emissions must be under control. For this reason, the estimation of CO 2 emissions has become an important factor for researchers and scientists. In this study, a new hybrid method was developed using optimization methods. The Shuffled Frog-Leaping Algorithm (SFLA) algorithm has recently become the preferred method for solving many optimization problems. SFLA, a swarm-based heuristic method, was developed in this study using the Levy flight method. Thus, the speed of reaching the optimum result of the algorithm has been improved. This method, which was developed later, was used in a hybrid structure of the Firefly Algorithm (FA). In the next step, a new Artificial Neural Network (ANN)-based estimation method is proposed using the hybrid optimization method. The method was used to estimate the amount of CO 2 emissions in Türkiye. The proposed hybrid model had the RMSE error 5.1107 and the R2 0.9904 for a testing dataset, respectively. In the last stage, Türkiye’s future CO 2 emission estimation is examined in three different scenarios. The obtained results show that the proposed estimation method can be successfully applied in areas requiring future estimation.

Keywords: carbon dioxide emissions; estimation; optimization; energy; green deal; metaheuristic algorithms; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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