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Forecasting of carbon dioxide emissions from power plants in Kuwait using United States Environmental Protection Agency, Intergovernmental panel on climate change, and machine learning methods

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  • AlKheder, Sharaf
  • Almusalam, Ali

Abstract

The second largest share of Greenhouse Gas (GHG) emissions is generated by electricity production. Approximately 63% of the generated electricity is from burning fossil fuels. Currently, The Ministry of Electricity and Water (MEW) owns and operates 8 power plants to secure the demand for electricity in Kuwait. Burning more fuel to generate electricity increases CO2 emissions to the air which causes air pollution and environmental issues. This study aims to calculate the amount of CO2 emission from the power sector specifically from each power plant in Kuwait in 2019 using combustion equation from United States Environmental Protection Agency (USEPA) and Intergovernmental panel on Climate Change (IPCC). According to USEPA, total CO2 emissions from the power sector in Kuwait in 2019 were found to be 38.47 MtCO2. However, IPCC equation gave total CO2 emissions of 45.57 MtCO2. The second part of the research focused on forecasting CO2 emissions for 5 years (2018–2022) using machine learning (ML) algorithms, which are mainly support vector machine (SVM), deep learning (DL), and ANN. Based on DL model results, the forecasted CO2 emissions for the 5 years were 44.2, 46, 48, 47, and 49 MtCO2, respectively. While ANN model showed the following CO2 emissions result for each year: 43, 44, 49, 51, and 50 MtCO2, respectively. Moreover, SVM algorithm found the forecasted CO2 emissions for the 5 years to be 43.8, 52 , 56, and 56 MtCO2, respectively. DL model was found to be the most appropriate one to fit the data followed by ANN and lastly SVM respectively.

Suggested Citation

  • AlKheder, Sharaf & Almusalam, Ali, 2022. "Forecasting of carbon dioxide emissions from power plants in Kuwait using United States Environmental Protection Agency, Intergovernmental panel on climate change, and machine learning methods," Renewable Energy, Elsevier, vol. 191(C), pages 819-827.
    • Handle: RePEc:eee:renene:v:191:y:2022:i:c:p:819-827
    DOI: 10.1016/j.renene.2022.04.023
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    References listed on IDEAS

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    1. Bader Alshuraiaan, 2021. "Renewable Energy Technologies for Energy Efficient Buildings: The Case of Kuwait," Energies, MDPI, vol. 14(15), pages 1-16, July.
    2. Al-Salem, S.M., 2015. "Carbon dioxide (CO2) emission sources in Kuwait from the downstream industry: Critical analysis with a current and futuristic view," Energy, Elsevier, vol. 81(C), pages 575-587.
    3. Al-Mutairi, Asma'a & Smallbone, Andrew & Al-Salem, S.M. & Roskilly, Anthony Paul, 2017. "The first carbon atlas of the state of Kuwait," Energy, Elsevier, vol. 133(C), pages 317-326.
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    Cited by:

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    2. Wentao Feng & Tailong Chen & Longsheng Li & Le Zhang & Bingyan Deng & Wei Liu & Jian Li & Dongsheng Cai, 2024. "Application of Neural Networks on Carbon Emission Prediction: A Systematic Review and Comparison," Energies, MDPI, vol. 17(7), pages 1-15, March.
    3. Sadi, Meisam & Alsagri, Ali Sulaiman & Rahbari, Hamid Reza & Khosravi, Soheil & Arabkoohsar, Ahmad, 2024. "Thermal energy demand decarbonization for the industrial sector via an innovative solar combined technology," Energy, Elsevier, vol. 292(C).
    4. Zhenfen Wu & Zhe Wang & Qiliang Yang & Changyun Li, 2024. "Prediction Model of Electric Power Carbon Emissions Based on Extended System Dynamics," Energies, MDPI, vol. 17(2), pages 1-22, January.
    5. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).

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