Artificial Intelligence and Machine Learning for Energy in South Africa

Farai Mlambo and David Mhlanga ()
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David Mhlanga: University of Johannesburg

Africagrowth Agenda, 2022, vol. 19, issue 4, 20-23

Abstract: The energy sector faces several worldwide difficulties, including increasing consumption, shifting supply and demand trends, and a lack of the necessary analytics for efficient management. These problems are more serious in developing countries especially in Africa. A considerable proportion of energy is not metered or paid for due to a large number of unauthorized connections to the power grid, which causes energy losses and increased CO2 emissions because users are less likely to use energy wisely when it is free. Artificial intelligence(AI) and similar technologies that enable interaction among power grid, smart appliances, and Industrial internet Of things have already been implemented in industrialized countries’ energy sectors. The goal of this study is to examine the potential contribution that AI and Machine Learning (ML) can make to enhancing energy output and increase efficiency in energy use in the South African energy sector. The study was motivated by the difficulties brought on by frequent load shedding and significant electrical shortages in South Africa. The analysis of how AI and ML might help South Africa’s energy sector concluded the study.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:afj:journ2:v:19:y:2022:i:4:p:20-23

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