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Journals / Journal of Energy Systems / 2026 / Volume: 10 Issue: 3

Forecasting Indonesia’s Coal Consumption Using Artificial Neural Network

Setiawan Titora Fallo(Hasanuddin University)0009-0004-5271-9372
Aryanti Virtanti Anas(Hasanuddin University)0000-0002-0682-1729
Rini Novrianti Sutardjo Tui(Hasanuddin University)0000-0001-9562-3945
Pages
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Publication Date
2026-09-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Forecasting results suggest a gradual increase in coal consumption, reaching approximately 343.96 million tonnes by 2032, and underscore the need for adaptive strategies in coal mining and coal-consuming sectors to maintain energy supply stability, address environmental sustainability challenges, and support national energy planning and policy decisions in the context of Indonesia's long-term decarbonization goals.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Indonesia is one of the world’s largest coal producers and exporters, with high domestic coal consumption, making reliable coal consumption forecasting essential for energy planning and policy development.
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Key Findings & Data:The model was trained using Levenberg–Marquardt (trainlm) and Bayesian Regularization (trainbr) algorithms with data normalized to the ranges of [0,1] and [-1,1].
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Conclusion & Impact:Forecasting results suggest a gradual increase in coal consumption, reaching approximately 343.96 million tonnes by 2032, and underscore the need for adaptive strategies in coal mining and coal-consuming sectors to maintain energy supply stability, address environmental sustainability challenges, and support national energy planning and policy decisions in the context of Indonesia's long-term decarbonization goals.
🔬Design:Modeling & Simulation
📍Scope:2032 Verileri
⚙️Variables:Artificial neural network · Bayesian regularization backpropagation · Coal consumption forecasting

Abstract

Indonesia is one of the world’s largest coal producers and exporters, with high domestic coal consumption, making reliable coal consumption forecasting essential for energy planning and policy development. This study applies an Artificial Neural Network (ANN) model using a Multilayer Perceptron (MLP) architecture to forecast coal consumption in Indonesia using population, Gross Domestic Product (GDP), exports, and imports as input variables. The model was trained using Levenberg–Marquardt (trainlm) and Bayesian Regularization (trainbr) algorithms with data normalized to the ranges of [0,1] and [-1,1]. Model performance was evaluated using correlation coefficient (R) and Mean Absolute Percentage Error (MAPE). Data normalized to the range [-1,1] when combined with the trainbr algorithm produced higher R values and lower MAPE. The optimal configuration, consisting of 9 hidden neurons, achieved an R value of 0.9991 and a MAPE of 2.90%. These results highlight the importance of data normalization and training algorithm selection in improving MLP model reliability. Forecasting results suggest a gradual increase in coal consumption, reaching approximately 343.96 million tonnes by 2032, and underscore the need for adaptive strategies in coal mining and coal-consuming sectors to maintain energy supply stability, address environmental sustainability challenges, and support national energy planning and policy decisions in the context of Indonesia's long-term decarbonization goals.

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Cite This Article

Fallo, S. T., Anas, A. V., Tui, R. N. S. (2026). Forecasting Indonesia’s Coal Consumption Using Artificial Neural Network. Journal of Energy Systems, Volume: 10 Issue: 3. https://doi.org/10.30521/jes.1930805

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References (34)

  1. Li, A. Analysis on the current situation and future outlook of coal market in Indonesia. In: EBLDM 2020 214. International Conference on Energy Big Data and Low-carbon Development Management; 18-20 December 2020: EDP Science, p. 02004
  2. Ministry of Energy and Mineral Resources Republic of Indonesia. Handbook of Energy and Economic Statistics of Indonesia 2024 (ISSN. 2538-3464). Ministry of Energy and Mineral Resources Republic of Indonesia 2025. https://www.esdm.go.id/assets/media/content/content-handbook-of-energy-and-economic-statistics-of-indonesia-2024.pdf
  3. Aimon, H., Kurniadi, A. P., Sentosa, S. U., Abd Rahman, N. Production, consumption, export and carbon emission for coal commodities: Cases of Indonesia and Australia. International Journal of Energy Economics and Policy 2023; 13(5): 484-492, https://doi.org/10.32479/ijeep.14798
  4. Mahroini, Z., Chien, Y. L. Carbon emission and environmental cost from coal production in Indonesia. Journal of Degraded and Mining Lands Management 2024; 11(4): 6387-6397, https://doi.org/10.15243/jdmlm.2024.114.6387
  5. Ngarayana, I. W., Sutanto, J., Murakami, K. Predicting the future of Indonesia: Energy, economic and sustainable environment development. In: MICES 2020 753. Medan International Conference on Energy and Sustainability; 27-28 October 2020: IOP Publishing, p. 012038
  6. Bermejo, J. F., Fernandez, J. F. G., Polo, F. O., Marquez, A. C. A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. Applied Sciences 2019; 9(9): 1844, https://doi.org/10.3390/app9091844
  7. Román-Portabales, A., López-Nores, M, Pazos-Arias, J. J. Systematic review of electricity demand forecast using ANN-based machine learning algorithms. Sensors 2021; 21(13): 4544, https://doi.org/10.3390/s21134544
  8. Lu, C., Li, S., Lu, Z. Building energy prediction using artificial neural networks: a literature survey. Energy and Buildings 2022; 262: 111718, https://doi.org/10.1016/j.enbuild.2021.111718
  9. Yin, Q., Han, C., Li, A., Liu, X., Liu, Y. A review of research on building energy consumption prediction models based on artificial neural networks. Sustainability 2024; 16(17): 7805, https://doi.org/10.3390/su16177805
  10. Wang, L., Zhan, L., Li, R. Prediction of the energy demand trend in middle Africa—a comparison of MGM, MECM, ARIMA and BP models. Sustainability 2019; 11(8): 2436, https://doi.org/10.3390/su11082436
  11. Wibowo, S. M., Hakim, D. B., Barus, B., Fauzi, A. Estimation of energy demand in Indonesia using artificial neural network. International Journal of Energy Economics and Policy 2022; 12(6): 261-271, https://doi.org/10.32479/ijeep.11390
  12. Bolat, E., Yildiz, Y. A. Energy demand and CO2 emission forecast model for Turkey with deep learning and machine learning algorithms. Elektronika ir Elektrotechnika 2025; 31(1): 12-21, https://doi.org/https://doi.org/10.5755/j02.eie.40288
  13. Köse, E., Kutlu Kaynar, S. Energy demand forecasting and policy development in Turkey. Energies 2025; 18(13): 3301, https://doi.org/10.3390/en18133301
  14. Benalcazar, P., Krawczyk, M., Kamiński, J. Forecasting global coal consumption: An artificial neural network approach, Gospodarka Surowcami Mineralnymi-Mineral Resources Management 2017; 33(4): 29-44, https://doi.org/10.1515/gospo-2017-0042
  15. Erdoğan, H., Kayakuş, M., Terzioğlu, M. Estimating coal consumption in Turkey using machine learning methods. International Journal of Oil, Gas and Coal Technology 2023; 33(1): 20-36, https://doi.org/10.1504/IJOGCT.2023.130373
  16. Saglam, M., Spataru, C., Karaman, O. A. Electricity demand forecasting with use of artificial intelligence: the case of Gokceada Island. Energies 2022; 15(16): 5950, https://doi.org/10.3390/en15165950
  17. da Silva, F. L. C., da Costa, K., Rodrigues, P. C., Salas, R., López-Gonzales, J. L. Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies 2022; 15(2): 588, https://doi.org/10.3390/ en15020588
  18. Bandyopadhyay, A., Sarkar, B. D., Hossain, M. E., Rej, S., Mallick, M. A. Modelling and forecasting India's electricity consumption using artificial neural networks. OPEC Energy Review 2024; 48(2): 65-77, https://doi.org/10.1111/opec.12295
  19. Yan, Y., Shao, Y., Wang, D., Yang, Z., Ma, H., Li, Q., Li, P. Prediction of the whole society electricity consumption in northeast China based on the BP neural network and Markov. Front. Energy Res. 2024; 12:1326525, https://doi.org/10.3389/fenrg.2024.1326525
  20. Kim, Y. S, Kim, M. K., Fu, N., Liu, J., Wang, J., Srebric, J. Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models. Sustainable Cities and Society 2025; 118: 105570, https://doi.org/10.1016/j.scs.2024.105570
  21. Hartono, Ahmad, A. M., Sadikin, M. Comparison methods of short-term electrical load forecasting, In: ICIEE 2018 218. International Conference on Industrial, Electrical and Electronics; 4-5 September 218: EDP Sciences, p. 01002
  22. Nawi, N. M., Hussein, A. S., Samsudin, N. A., Hamid, N. A., Yunus, M. A. M., Ab Aziz, M. F. The effect of pre-processing techniques and optimal parameters selection on back propagation neural networks. International Journal on Advanced Science, Engineering and Information Technology 2017; 7(3): 770-777, https://doi.org/10.18517/ijaseit.7.3.2074
  23. Tian, S., Arshad. N. I., Toghraie, D., Eftekhari, S. A., Hekmatifar, M. Using perceptron feed-forward artificial neural network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid. Case Studies in Thermal Engineering 2021; 26: 101055, https://doi.org/10.1016/j.csite.2021.101055
  24. BPS-Statistics Indonesia. Statistical Yearbook of Indonesia 2024 (Publication No. 03200.24003). BPS-Statistics Indonesia 2024. https://www.bps.go.id/id/publication/2024/02/28/c1bacde03256343b2bf769b0/statistik-indonesia-2024.html
  25. Chan, J. Y. -L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z., -W., Chen, Y., -L. Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics 2022; 10(8): 1283, https://doi.org/10.3390/ math10081283
  26. Reddy, M. C., Naresh, M. Multicollinearity in machine learning: detection, impact, and remedial techniques. International Journal of Advances In Signal and Image Sciences 2026; 12(3): 499–510, https://doi.org/10.29284/sg8jdw24
  27. Li, S., Yang, X., Li, R. Forecasting coal consumption in India by 2030: using linear modified linear (MGM-ARIMA) and linear modified nonlinear (BP-ARIMA) combined models. Sustainability 2019; 11(3): 695, https://doi.org/10.3390/su11082436
  28. Beale, MH, Hagan, MT, Demuth, HB. Deep Learning Toolbox™ Reference. Natick, MA: The MathWorks, Inc, 2021.
  29. Basnayake, B. R. P. M., Chandrasekara, N. V. Application of random data splitting technique for time series modeling using feedforward neural network and support vector regression models. Journal of Applied Data Sciences 2026; 7(2): 1496-1515, https://doi.org/10.47738/jads.v7i2.1216
  30. Resosudarmo, B. P., Rezki, J. F., Effendi, Y. Prospects of Energy Transition in Indonesia. Bulletin of Indonesian Economic Studies 2023; 59(2): 149-177, https://doi.org/10.1080/00074918.2023.2238336
  31. Handayani, E., Maharani, V. Harmonizing Indonesia’s regulatory framework on energy transition to balance green goals and economic growth. In: ICOLEG-2025 1537. International Conference on Law, Economic, and Governance; 3–4 June 2025: IOP Publishing, p. 012003
  32. Hersaputri, L. D., Yeganyan, R., Cannone, C., Plazas-Niño, F., Osei-Owusu, S., Kountouris, Y., Howells, M. Reducing fossil fuel dependence and exploring just energy transition pathways in Indonesia using OSeMOSYS (Open-Source Energy Modelling System). Climate 2024; 12(3): 37, https://doi.org/10.3390/cli12030037
  33. Rahma, L., Hartono, D., Hastuti S. H. Carbon tax implementation in Indonesia: a social accounting matrix analysis. Sustainability: Science, Practice and Policy 2025; 21:1, 2454061, https://doi.org/10.1080/15487733.2025.2454061
  34. Chen, C., Inamdar, M. N., Al-Odaini, A. Decoupling carbon emissions from power sector growth in Indonesia. Discover Environment 2025; 3(1), 1-14, https://doi.org/10.1007/s44274-025-00426-0 [35] Siregar, Y. I. Pathways towards net-zero emissions in Indonesia's energy sector. Energy 2024; 308: 133014, https://doi.org/10.1016/j.energy.2024.133014