Explainable AI (XAI) in Smart Grids for Predictive Maintenance: A survey

Peter Onu, Anup Pradhan, Nelson Sizwe Madonsela

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The dynamic trend of modern energy infrastructure demands proactive and transparent solutions, especially in predictive maintenance for smart grids. This research discusses the integration of Explainable AI (XAI) to augment the reliability and trustworthiness of predictive maintenance strategies within smart grids. As such, the present study explores how XAI can be better understood based on predictive maintenance procedures and delignates the factors influencing maintenance decisions. In addition, the paper highlights the implications of two XAI techniques (LIME and SHAP) and then surveys recent literature on the subject matter. The authors are optimistic that this paper will spark a new turn towards, as per stakeholders' commitment to enhance the operational efficiency of energy infrastructure with emphasis on the decision-making processes that drive these critical systems.

Original languageEnglish
Title of host publication1st International Conference on Smart Energy Systems and Artificial Intelligence, SESAI 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350349689
DOIs
Publication statusPublished - 2024
Event1st International Conference on Smart Energy Systems and Artificial Intelligence, SESAI 2024 - Mauritius, Mauritius
Duration: 3 Jun 20246 Jun 2024

Publication series

Name1st International Conference on Smart Energy Systems and Artificial Intelligence, SESAI 2024

Conference

Conference1st International Conference on Smart Energy Systems and Artificial Intelligence, SESAI 2024
Country/TerritoryMauritius
CityMauritius
Period3/06/246/06/24

Keywords

  • and opportunities
  • challenges
  • explainable AI
  • predictive maintenance
  • smart grid

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

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