Supervisor
Dr. Kislay Raj & Dr. Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study evaluates advanced time-series forecasting models to predict greenhouse gas emissions (GHGE) in Ireland's residential sector. LSTM, XGBoost, and ARIMA models were tested alongside feature selection methods including PCA, XGBoost-based importance, and Granger causality. Urban population growth and electricity consumption emerged as the most significant predictors. While LSTM struggled due to limited data, XGBoost showed strong predictive performance (MAPE ~9%), and ARIMA with key features achieved the highest accuracy (MAPE 3.78%). Forecasts indicate a declining GHGE trend, offering actionable insights for environmental planning and policy in the residential sector.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Aldeen Salman, S.
(2025) Identifying and Forecasting Key Drivers of Greenhouse Gas Emissions in Ireland's Residential Sector Multivariate Time Series Analysis. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.104