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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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. Cao, Qingfeng & Chi, Chuenyu & Shan, Junhui, 2025. "Can artificial intelligence technology reduce carbon emissions? A global perspective," Energy Economics, Elsevier, vol. 143(C).
    4. 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).
    5. 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.
    6. 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.

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