IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2022-06-33.html
   My bibliography  Save this article

Estimation of Energy Demand in Indonesia using Artificial Neural Network

Author

Listed:
  • Satrio Mukti Wibowo

    (Ministry of Energy and Mineral Resources, Jakarta, 10110, Indonesia,)

  • Dedi Budiman Hakim

    (Faculty of Economy and Management, Bogor Agricultural University, Bogor 16680, Indonesia,)

  • Baba Barus

    (Department of Soil and Land Resources, Faculty of Agriculture, Bogor Agricultural University, Bogor 16680, Indonesia.)

  • Akhmad Fauzi

    (Faculty of Economy and Management, Bogor Agricultural University, Bogor 16680, Indonesia,)

Abstract

Although Indonesia has many variations in energy types, Indonesia is currently a Net Oil Importer Country. Therefore, accurate energy demand estimation is very important for energy policy making in Indonesia. This study proposes a neural network model to efficiently, precisely and validly estimate energy demand for Indonesia. This model has four independent variables, such as gross domestic product (GDP), population, imports, and exports. Data obtained from Central Bureau of Statistics of Indonesia and The Ministry of Energy and Mineral Resources. Energy estimation is using a pessimistic, realistic and optimistic scenario that estimates of energy demand in the next 10 years using artificial neural networks shows that energy demand in Indonesia continues to increase every year, both in pessimistic, realistic and optimistic scenarios.

Suggested Citation

  • Satrio Mukti Wibowo & Dedi Budiman Hakim & Baba Barus & Akhmad Fauzi, 2022. "Estimation of Energy Demand in Indonesia using Artificial Neural Network," International Journal of Energy Economics and Policy, Econjournals, vol. 12(6), pages 261-271, November.
    • Handle: RePEc:eco:journ2:2022-06-33
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/11390/7021
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/11390
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geem, Zong Woo & Roper, William E., 2009. "Energy demand estimation of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 37(10), pages 4049-4054, October.
    2. Xavier Labandeira & José M. Labeaga & Miguel Rodríguez, 2006. "A Residential Energy Demand System for Spain," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 87-112.
    3. Hunt, Lester C. & Ryan, David L., 2015. "Economic modelling of energy services: Rectifying misspecified energy demand functions," Energy Economics, Elsevier, vol. 50(C), pages 273-285.
    4. Duran Toksari, M., 2007. "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, Elsevier, vol. 35(8), pages 3984-3990, August.
    5. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    6. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    7. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Srikaew, Artit, 2011. "Projection of future transport energy demand of Thailand," Energy Policy, Elsevier, vol. 39(5), pages 2754-2763, May.
    8. Mohamed, Zaid & Bodger, Pat, 2005. "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, Elsevier, vol. 30(10), pages 1833-1843.
    9. Gundimeda, Haripriya & Kohlin, Gunnar, 2008. "Fuel demand elasticities for energy and environmental policies: Indian sample survey evidence," Energy Economics, Elsevier, vol. 30(2), pages 517-546, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
    2. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    3. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    4. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    5. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    6. Wu, Qunli & Peng, Chenyang, 2017. "A hybrid BAG-SA optimal approach to estimate energy demand of China," Energy, Elsevier, vol. 120(C), pages 985-995.
    7. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    8. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    9. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting i," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    10. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
    11. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.
    12. Adom, Philip Kofi & Amakye, Kwaku & Barnor, Charles & Quartey, George & Bekoe, William, 2016. "Shift in demand elasticities, road energy forecast and the persistence profile of shocks," Economic Modelling, Elsevier, vol. 55(C), pages 189-206.
    13. Geem, Zong Woo, 2011. "Transport energy demand modeling of South Korea using artificial neural network," Energy Policy, Elsevier, vol. 39(8), pages 4644-4650, August.
    14. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
    15. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    16. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    17. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    18. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
    19. Al-Ghandoor, Ahmed & Samhouri, Murad & Al-Hinti, Ismael & Jaber, Jamal & Al-Rawashdeh, Mohammad, 2012. "Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique," Energy, Elsevier, vol. 38(1), pages 128-135.
    20. Muhammad Irfan & Michael P. Cameron & Gazi Hassan, 2017. "Household Energy Elasticities in Pakistan: An Application of the LA-AIDS Model on Pooled Household Data," Working Papers in Economics 17/11, University of Waikato.

    More about this item

    Keywords

    energy demand; energy policy; artificial neural networks;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ2:2022-06-33. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.