Optimizing Energy Consumption Prediction Across the IMT-GT Region Through PCA-Based Modeling

Muhammad Farid; Teuku Muhammad Faiz Nuzullah; Zatul Aklya; Syifa Nazila; Muhammad Zia Ulhaq; Feby  Apriliansyah; Novi Reandy Sasmita

doi:10.60084/ijds.v3i1.286

Authors

Muhammad Farid Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Teuku Muhammad Faiz Nuzullah Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Zatul Aklya Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Syifa Nazila Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Muhammad Zia Ulhaq Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Feby Apriliansyah Department of Regional and Urban Planning, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Novi Reandy Sasmita Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/ijds.v3i1.286

Keywords:

Principal Component Analysis, Random Forest, Energy consumption, Prediction , Multicollinearity

Abstract

This study aims to improve the accuracy of energy consumption prediction in the Indonesia-Malaysia-Thailand Growth Triangle (IMT-GT) region by addressing multicollinearity among independent variables such as energy production (Mtoe), lignite coal production (million tons), crude oil production (million tons), refined oil production (million tons), natural gas production (billion cubic meters), and electricity production (terawatt-hours). By integrating Principal Component Analysis (PCA) with Random Forest (RF), six correlated variables were reduced into two uncorrelated principal components (PC1 and PC2), explaining 80.77% of the data variance. The PCA-RF hybrid model outperformed the standalone Random Forest (RF) model, with an increase in the coefficient of determination (R²) from 0.976 to 0.993. Additionally, it achieved significant reductions in error metrics, with the mean absolute error (MAE) decreasing from 5.811 to 4.169 and the root mean square error (RMSE) dropping from 9.278 to 4.786. These results demonstrate PCA’s effectiveness in isolating dominant drivers such as energy and lignite coal production while improving model stability. The framework provides policymakers with a reliable tool to forecast energy demand and align economic growth with sustainability in fossil fuel-dependent economies.

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