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Predictive modelling of energy consumption in Malaysia: A regression analysis approach

Global energy consumption is influenced by various human activities, including fossil fuel-based energy generation, household energy usage, and population growth. This case study aims to identify and predict key factors in energy consumption in Malaysia using Regression Analysis. The dataset spans...

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Main Authors: Suhaila, Bahrom, Aisyah Amalina, Mohd Noor, Anis Farehan, Muhammad Fakihi
Format: Article
Language:English
Published: Zenodo 2024
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/42162/1/Predictive%20Modelling%20of%20Energy%20Consumption%20in%20Malaysia%20A%20Regression%20Analysis%20Approach.pdf
http://umpir.ump.edu.my/id/eprint/42162/
https://doi.org/10.5281/zenodo.12791157
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spelling my.ump.umpir.421622024-08-05T04:20:49Z http://umpir.ump.edu.my/id/eprint/42162/ Predictive modelling of energy consumption in Malaysia: A regression analysis approach Suhaila, Bahrom Aisyah Amalina, Mohd Noor Anis Farehan, Muhammad Fakihi Q Science (General) QA Mathematics Global energy consumption is influenced by various human activities, including fossil fuel-based energy generation, household energy usage, and population growth. This case study aims to identify and predict key factors in energy consumption in Malaysia using Regression Analysis. The dataset spans from 2000 to 2020 and includes variables such as access to electricity, renewable energy capacity, electricity from renewables, access to clean cooking fuels, renewable energy share in total consumption, and primary energy consumption per capita. The R software was used to analyse the data. According to the analysis, the predictor variables that are correlated with the primary energy consumption are renewable electricity generating capacity, electricity from renewables, access to clean fuels for cooking, and renewable energy share in total final energy consumption. The findings suggest that increasing the share of renewable energy sources and improving access to clean cooking fuels could potentially reduce overall energy consumption in Malaysia. The regression model developed in this study can be a valuable tool for policymakers and energy planners to forecast future energy demand and formulate strategies to promote sustainable energy usage. Furthermore, the methodology employed can be adapted to analyze energy consumption patterns in other countries or regions, facilitating a deeper understanding of the factors driving global energy consumption. Zenodo 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42162/1/Predictive%20Modelling%20of%20Energy%20Consumption%20in%20Malaysia%20A%20Regression%20Analysis%20Approach.pdf Suhaila, Bahrom and Aisyah Amalina, Mohd Noor and Anis Farehan, Muhammad Fakihi (2024) Predictive modelling of energy consumption in Malaysia: A regression analysis approach. APS Proceedings, 13. pp. 71-76. (Published) https://doi.org/10.5281/zenodo.12791157 10.5281/zenodo.12791157
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Suhaila, Bahrom
Aisyah Amalina, Mohd Noor
Anis Farehan, Muhammad Fakihi
Predictive modelling of energy consumption in Malaysia: A regression analysis approach
description Global energy consumption is influenced by various human activities, including fossil fuel-based energy generation, household energy usage, and population growth. This case study aims to identify and predict key factors in energy consumption in Malaysia using Regression Analysis. The dataset spans from 2000 to 2020 and includes variables such as access to electricity, renewable energy capacity, electricity from renewables, access to clean cooking fuels, renewable energy share in total consumption, and primary energy consumption per capita. The R software was used to analyse the data. According to the analysis, the predictor variables that are correlated with the primary energy consumption are renewable electricity generating capacity, electricity from renewables, access to clean fuels for cooking, and renewable energy share in total final energy consumption. The findings suggest that increasing the share of renewable energy sources and improving access to clean cooking fuels could potentially reduce overall energy consumption in Malaysia. The regression model developed in this study can be a valuable tool for policymakers and energy planners to forecast future energy demand and formulate strategies to promote sustainable energy usage. Furthermore, the methodology employed can be adapted to analyze energy consumption patterns in other countries or regions, facilitating a deeper understanding of the factors driving global energy consumption.
format Article
author Suhaila, Bahrom
Aisyah Amalina, Mohd Noor
Anis Farehan, Muhammad Fakihi
author_facet Suhaila, Bahrom
Aisyah Amalina, Mohd Noor
Anis Farehan, Muhammad Fakihi
author_sort Suhaila, Bahrom
title Predictive modelling of energy consumption in Malaysia: A regression analysis approach
title_short Predictive modelling of energy consumption in Malaysia: A regression analysis approach
title_full Predictive modelling of energy consumption in Malaysia: A regression analysis approach
title_fullStr Predictive modelling of energy consumption in Malaysia: A regression analysis approach
title_full_unstemmed Predictive modelling of energy consumption in Malaysia: A regression analysis approach
title_sort predictive modelling of energy consumption in malaysia: a regression analysis approach
publisher Zenodo
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42162/1/Predictive%20Modelling%20of%20Energy%20Consumption%20in%20Malaysia%20A%20Regression%20Analysis%20Approach.pdf
http://umpir.ump.edu.my/id/eprint/42162/
https://doi.org/10.5281/zenodo.12791157
_version_ 1822924567864147968
score 13.235796

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