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

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

Energies, 2023, vol. 16, issue 2, 1-17

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.

Keywords: artificial intelligence; energy sector; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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