A Prototype Ground Support System Security Monitor for Space Based Power System Health Monitoring

Janik, D.F.; Gholdston, E.W.

doi:10.4271/929332

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…1990-1999: There are a total of 47 works in this period. These include the impact of AI on power plant reliability (Christie, 1990), power management and distribution for automation (Ashworth, 1990), nuclear power plant (Trovato et al, 1990), explainability of electric circuits (Kashihara et al, 1992), AI in power systems management (Janik and Gholdston, 1992;Hasan et al, 1994;Irisarri, 1996), short-term electric load forecasting (Stroemich and Thomas, 1997), reliability of nuclear weapons stockpile protection measures (Molley, 1996), AI in fault diagnosis systems for power plants (Kiupel et al, 1995), electricity transportation management (Jennings, 1995), decision tree interpretability to manage electric power utilities (Wehenkel et al, 1994), explainability for the evaluation of power system security (Boyen and Wehenkel, 1999), and trustworthy intelligent agents to power markets (Krishna and Ramesh, 1998). 2000-2009: There are a total of 168 works in this period.…”

Section: Research In Ai and Energy Systems (1967-2022)mentioning

confidence: 99%

AI explainability and governance in smart energy systems: A review

2023

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Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.

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