Predicting CO2 Emission from Power Industry using Machine Learning

Singh, Pooja (2022) Predicting CO2 Emission from Power Industry using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Carbon dioxide is the primary contributor to global warming, which has had a devastating impact on both economic growth and human well-being. People and the government are working together to address climate change as a global issue so that our descendants don't have to bear the consequences. Even though Greenhouse Gas (GHG) emissions have decreased, the United States (U.S.) continues to be one of the top GHG emitters. Approximately 80% of Greenhouse Gas (GHG) emissions come from carbon dioxide (CO2). As a result, the decrease in CO2 emissions will contribute to reducing GHG emissions produced in the United States each year. from the US Energy Information Administration (EIA). The pre-processed data shows that the coal, natural gas, and total energy electric power sectors are the three highest emitters of CO2. Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), Prophet, and exponential smoothing models employ the emissions from these three sectors to anticipate CO2 emissions. It was shown that the Triple Exponential Smoothing model surpasses the all the other models with a MAE of 1.97 when it comes to projecting CO2 emissions from different industries, according to the data. Therefore, the data will be presented to structural engineers, builders, and the government to assist them design successful plans and policies for decarbonization.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Seasonal Autoregressive Integrated Moving Average (SARIMA); Long Short-Term Memory (LSTM)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TD Environmental technology. Sanitary engineering G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	13 Mar 2023 10:42
Last Modified:	13 Mar 2023 10:42
URI:	https://norma.ncirl.ie/id/eprint/6314

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