Predicting Electricity Consumption in Aceh Province Using the Markov Chain Monte Carlo Method

Virza Gavinda, Nurdin Nurdin, Fajriana Fajriana

Abstract


Electricity is essential to nearly every aspect of modern life, from industrial sectors to household needs. In Aceh Province, the demand for electricity has consistently increased along with economic growth, urbanization, and population expansion. Various studies indicate that rising electricity consumption is closely linked to economic growth and industrialization. This study uses the Markov Chain Monte Carlo (MCMC) method with the Metropolis-Hastings algorithm to predict electricity consumption in Aceh Province. The research addresses the significant increase in electricity consumption driven by economic growth and urbanization in the region. Electricity consumption data from January 2018 to December 2022 was utilized as the basis for modeling. The results indicate a 32.4% increase in electricity consumption over the past five years. The predictive model achieved high accuracy with a Mean Absolute Percentage Error (MAPE) of 2.41%, demonstrating its reliability in forecasting future electricity needs. Projections through 2030 show a continuous increase, reaching 482 GWh by the end of the period. These findings are expected to support decision-making in sustainable energy planning and providing adequate electricity infrastructure in Aceh. This study highlights the effectiveness of the Me-tropolis-Hastings algorithm in handling complex data with high variability, providing valuable insights for long-term energy planning

Keywords


Electricity Consumption Prediction, Markov Chain Monte Carlo, Metropolis-Hastings, Energy Planning, Aceh

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References


International Energy Agency (IEA), World Energy Outlook 2019. Paris: IEA Publications, 2019.

OECD, OECD Economic Outlook 2020. OECD Publishing, 2020.

Y. Zhou, et al., "A Review of Energy Prediction Techniques," Energy Reports, vol. 5, pp. 123-145, 2019.

D. Ekananta, M. Santoso, and B. Pradana, "Average-Based Fuzzy Time Series for Predicting Electricity Consumption in Indone-sia," Journal of Applied Energy Research, vol. 16, no. 3, pp. 201-211, 2018.

A. Harisandy, "Forecasting Household Electricity Consumption in Indonesia using VAR and ARIMA Models," Indonesian Journal of Energy, vol. 7, no. 2, pp. 123-136, 2021.

D. Purnama and E. Siregar, "Projection of Electricity Consumption in Aceh Tamiang until 2030," Energy Journal of Sumatra, vol. 6, no. 1, pp. 45-52, 2015.

F. Nugraha and D. Purnama, "Predicting Electricity Consumption in Aceh using Adaptive Neuro-Fuzzy Inference System (AN-FIS)," Journal of Regional Energy Studies, vol. 8, no. 2, pp. 151-162, 2020.

Y. Zhang, T. Li, and W. Ma, "Support Vector Regression Optimized by Dragonfly Algorithm for Electricity Demand Forecasting in China," Energy Prediction Journal, vol. 11, no. 4, pp. 389-398, 2017.

S. Wang, et al., "AI Applications in Energy Demand Forecasting," Renewable Energy Journal, vol. 35, pp. 56-70, 2021.

A. Ansharulhaq, N. Nurdin, and Z. Yunizar, "News Popularity Prediction in West Sumatera Using Autoregressive Integrated Moving Average," International Journal of Engineering, Science and Information Technology, vol. 5, no. 1, pp. 19-27, 2025. doi:10.52088/ijesty.v5i1.623

A. Prasetyo, N. Nurdin, and H. A. K. Aidilof, "Comparison of Triple Exponential Smoothing and ARIMA in Predicting Crypto-currency Prices," International Journal of Engineering, Science and Information Technology, vol. 4, no. 4, pp. 63-71, 2024. doi:10.52088/ijesty.v4i4.577

Q. Yan, et al., "Markov Chain Monte Carlo Based Energy Use Behaviors Prediction of Office Occupants," Algorithms, 2020.

M. Hariadi, et al., "Prediction of Stock Prices Using Markov Chain Monte Carlo," CENIM 2020, 2020.

A. Dukunde, et al., "Prediction of Prevalence of Type 2 Diabetes in Rwanda Using the Metropolis-Hastings Sampling," African Health Sciences, 2021.

Z. Wang and Y. Nishi, "Stochastic Model for Simulating Levels of Polychlorinated Biphenyls in Small Tuna and Planktons Us-ing Metropolis-Hastings Algorithm," Ecotoxicology and Environmental Safety, 2022.

Y. R. Fan, et al., "Towards Reliable Uncertainty Quantification for Hydrologic Predictions, Part I: Development of a Particle Copula Metropolis-Hastings Method," Journal of Hydrology, 2022.

E. Yildirim, M. Yasar, and A. Aslan, "Energy Consumption and Economic Growth in the USA: Evidence from Renewable Ener-gy," Renewable Energy Reviews, vol. 29, no. 4, pp. 2313-2321, 2019.

International Renewable Energy Agency (IRENA), Renewable Energy in Developing Regions. IRENA Report, 2020.

C. Chatfield, The Analysis of Time Series: An Introduction, 6th ed. Boca Raton, FL: Chapman and Hall/CRC, 2004.

R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications: With R Examples, 4th ed. New York: Springer, 2017.

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis, 3rd ed. Boca Raton, FL: CRC Press, 2014.

S. Brooks, A. Gelman, G. Jones, and X. L. Meng, Handbook of Markov Chain Monte Carlo. Boca Raton, FL: CRC Press, 2011.

J. S. Rosenthal, A First Look at Stochastic Processes. Singapore: World Scientific, 2019.

C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd ed. New York: Springer, 2004.

M. A. A. Turkman, C. D. Paulino, and P. Müller, Computational Bayesian Statistics: An Introduction. Cambridge, U.K.: Cam-bridge University Press, 2019.

D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis, 5th ed. New York, NY: Wiley, 2015.




DOI: https://doi.org/10.52088/ijesty.v5i1.678

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International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674