IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p745-d1029301.html
   My bibliography  Save this article

Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review

Author

Listed:
  • David Mhlanga

    (College of Business and Economics, The University of Johannesburg, P.O. Box 524, Johannesburg 2006, South Africa)

Abstract

An increase in consumption and inefficiency, fluctuating trends in demand and supply, and a lack of critical analytics for successful management are just some of the problems that the energy business throughout the world is currently facing. This study set out to assess the potential contributions that AI and ML technologies could make to the expansion of energy production in developing countries, where these issues are more pronounced because of the prevalence of numerous unauthorized connections to the electricity grid, where a large amount of energy is not being measured or paid for. This study primarily aims to address issues that arise due to frequent power outages and widespread lack of access to energy in a wide range of developing countries. Findings suggest that AI and ML have the potential to make major contributions to the fields of predictive turbine maintenance, energy consumption optimization, grid management, energy price prediction, and residential building energy demand and efficiency assessment. A discussion of what has to be done so that developing nations may reap the benefits of artificial intelligence and machine learning in the energy sector concluded the paper.

Suggested Citation

  • David Mhlanga, 2023. "Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review," Energies, MDPI, vol. 16(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:745-:d:1029301
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/745/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/745/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Xu & Yuchen Pan & Wenting Chen & Hongyong Fu, 2019. "Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    3. David Mhlanga, 2021. "Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    4. David Mhlanga, 2022. "The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals," IJERPH, MDPI, vol. 19(3), pages 1-22, February.
    5. Baloko Makala & Tonci Bakovic, 2020. "Artificial Intelligence in the Power Sector," World Bank Publications - Reports 34303, The World Bank Group.
    6. David Mhlanga, 2021. "Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment," IJFS, MDPI, vol. 9(3), pages 1-16, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hassan Qudrat-Ullah, 2025. "A Thematic Review of AI and ML in Sustainable Energy Policies for Developing Nations," Energies, MDPI, vol. 18(9), pages 1-26, April.
    2. Islam, Md. Monirul & Shahbaz, Muhammad & Ahmed, Faroque, 2024. "Robot race in geopolitically risky environment: Exploring the Nexus between AI-powered tech industrial outputs and energy consumption in Singapore," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    3. Nazir, Kashif & Memon, Shazim Ali & Saurbayeva, Assemgul, 2024. "A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building," Applied Energy, Elsevier, vol. 376(PA).
    4. Rohit Agrawal & Abhijit Majumdar & Anil Kumar & Sunil Luthra, 2023. "Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities," Operations Management Research, Springer, vol. 16(4), pages 1720-1741, December.
    5. FU, Yunyun & SHEN, Yongchang & SONG, Malin & WANG, Weiyu, 2024. "Does artificial intelligence reduce corporate energy consumption? New evidence from China," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 548-561.

    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. David Mhlanga, 2022. "Stakeholder Capitalism, the Fourth Industrial Revolution (4IR), and Sustainable Development: Issues to Be Resolved," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    2. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    3. Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    4. Guillaume Edou Tchidi & Wei Zhang, 2025. "Mediating effect of financial inclusion on FinTech innovations and economic development in West Africa: Evidence from the Benin Republic," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(2), pages 1032-1048, April.
    5. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    6. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    7. Livio Cricelli & Serena Strazzullo, 2021. "The Economic Aspect of Digital Sustainability: A Systematic Review," Sustainability, MDPI, vol. 13(15), pages 1-15, July.
    8. Bhagwan, N. & Evans, M., 2023. "A review of industry 4.0 technologies used in the production of energy in China, Germany, and South Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2022. "l 1 -Regularization in Portfolio Selection with Machine Learning," Mathematics, MDPI, vol. 10(4), pages 1-15, February.
    10. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    11. Andres Alonso-Robisco & Jose Carbo & Emily Kormanyos & Elena Triebskorn, 2025. "Houston, we have a problem: can satellite information bridge the climate-related data gap?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.
    12. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    13. Wei Xu & Hongyong Fu & Huanpeng Liu, 2019. "Evaluating the Sustainability of Microfinance Institutions Considering Macro-Environmental Factors: A Cross-Country Study," Sustainability, MDPI, vol. 11(21), pages 1-22, October.
    14. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    15. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    16. Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.
    17. Yeguan Yu, 2023. "The Impact of Financial System on Carbon Intensity: From the Perspective of Digitalization," Sustainability, MDPI, vol. 15(2), pages 1-22, January.
    18. Chih-Hsiung Chang & Wu-Hua Chang & Yi-Yu Shih, 2022. "Is Financial Institution Management Effective to Reduce Problems Related to Information Asymmetry in Taiwan?," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 37-58.
    19. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    20. Lukasz Mach & Dariusz Zmarzly & Ireneusz Dabrowski & Pawel Fracz, 2020. "Comparison on Subannual Seasonality of Building Construction in European Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 241-257.

    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:gam:jeners:v:16:y:2023:i:2:p:745-:d:1029301. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.