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

Sharaf AlKheder and Ali Almusalam

Renewable Energy, 2022, vol. 191, issue C, 819-827

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.

Keywords: Carbon dioxide emissions; Power plants; US EPA & IPCC; Deep learning; Support vector machine; Artificial neural network (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:191:y:2022:i:c:p:819-827

DOI: 10.1016/j.renene.2022.04.023

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