Predictive modelling of energy consumption in Malaysia: A regression analysis approach - Suhaila, Bahrom; Aisyah Amalina, Mohd Noor; Anis Farehan, Muhammad Fakihi (Zenodo, 2024)
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.