Prospects for artificial intelligence to manage load-shedding in South Africa
| dc.contributor.author | Shakoane, Nomea Lerato | |
| dc.contributor.supervisor | Lee, Gregory | |
| dc.date.accessioned | 2024-09-11T12:10:59Z | |
| dc.date.available | 2024-09-11T12:10:59Z | |
| dc.date.issued | 2022 | |
| dc.description | A research article submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfillment of the requirements for the degree of Master of Business Administration Johannesburg, 2022 | |
| dc.description.abstract | Eskom, a state-owned utility in South Africa, is currently facing significant challenges and experiencing severe power shortages. While there is a growing expectation of adopting renewable energy in the future, a sudden and complete transition is unlikely. Legacy power systems, characterized by poor performance, breakdowns, and unpredictability, have received limited attention in AI research. This raises the question: What actions should be taken to quickly address maintenance issues in older power plants and increase generation capacity in the short term? The objective of this study is to explore AI solutions in the electrical sector and assess the feasibility and cost-effectiveness of integrating AI into Eskom's power system. The findings of this study will provide Eskom and the South African government with valuable insights to make informed decisions regarding the incorporation of artificial intelligence. These AI solutions can include detecting power and cable theft, optimizing energy usage and distribution, and implementing predictive analytics for demand planning and power production optimization. To gather data, a survey questionnaire was distributed to participants primarily located in South Africa, following a snowball selection process. The survey collected responses from a minimum of 50 participants and covered various aspects, such as load shedding at Eskom, artificial intelligence, data-AI enablers, and AI prospects. The study revealed that inadequate maintenance within the power generation division was responsible for load shedding. As a result, the implementation of AI solutions such as predictive maintenance, fault detection, and power demand monitoring systems emerged as crucial priorities for Eskom. However, it is important to note that implementing AI requires substantial capital investment. Considering Eskom's current financial situation and South Africa's mounting debt, it is challenging for Eskom to secure the necessary funds without seeking support vi from the South African government or major corporations like the IMF or World Bank | |
| dc.description.submitter | MM2024 | |
| dc.faculty | Faculty of Commerce, Law and Management | |
| dc.identifier.citation | Shakoane, Nomea Lerato. (2023). Prospects for artificial intelligence to manage load-shedding in South Africa [Master’s dissertation, University of the Witwatersrand, Johannesburg]. WireDSpace.https://hdl.handle.net/10539/40725 | |
| dc.identifier.uri | https://hdl.handle.net/10539/40725 | |
| dc.language.iso | en | |
| dc.publisher | University of the Witwatersrand, Johannesburg | |
| dc.rights | © 2022 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg. | |
| dc.rights.holder | University of the Witwatersrand, Johannesburg | |
| dc.school | WITS Business School | |
| dc.subject | Power shortages | |
| dc.subject | Renewable energy | |
| dc.subject | Legacy power systems | |
| dc.subject | AI solutions | |
| dc.subject | AI into Eskom's power system | |
| dc.subject | Artificial intelligence | |
| dc.subject | UCTD | |
| dc.subject.other | SDG-7: Affordable and clean energy | |
| dc.title | Prospects for artificial intelligence to manage load-shedding in South Africa | |
| dc.type | Dissertation |