Forecasting Carbon Dioxide Emission from Energy Consumption within the Industrial Sector in U.S.

Sawant, Vineet Manoj (2022) Forecasting Carbon Dioxide Emission from Energy Consumption within the Industrial Sector in U.S. Masters thesis, Dublin, National College of Ireland.

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Abstract

With carbon dioxide emission on the ascent as of late it's become more significant than any other time in recent memory for stakeholders to gauge future emission patterns and agree on approaches and methodologies, they carry out to limit the impacts. The U.S. has been one of the top emitters of carbon dioxide and its industrial sector has the highest share in it. Reduced carbon dioxide emissions would aid in the action required to address the climate change crisis. The data for 13 industrial sectors in the US was collected from the U.S. Energy Information Administration (EIA) website. Time series forecasting models like Simple Exponential Smoothing (SES), Holt-Winter Smoothing (HW), ARIMA, Prophet and LSTM were applied on the time series data of 13 industrial sectors. The models were compared using MAE, MAPE and RMSE and the best model was used to perform the short-term (six months) forecast for each sector.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	SES; HW; ARIMA; Prophet; LSTM; Carbon dioxide forecasting
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences 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
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	10 Mar 2023 17:27
Last Modified:	10 Mar 2023 17:27
URI:	https://norma.ncirl.ie/id/eprint/6297

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