Risk evaluation of AI systems in the energy sector - three case studies from TSO business
Overview & Synopsis
Artificial intelligence (AI) systems are nowadays widely used in electricity systems. The rising awareness of its potential risks requires that regulators and policy makers assess and compare AI systems considering criteria regarding their systemic criticality. Such evaluation is fundamental for a risk-aware use of AI systems and the timely development of mitigation strategies that ensure the safe and secure operation of the electricity sector. In this work, we introduce a practicable evaluation framework for AI systems in electricity systems. Using three case studies from electricity transmission system operators, we demonstrate the applicability of our framework.
Indexed from repository: HAL Open Science
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hal.science)
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Document Information
| Document Title | Risk evaluation of AI systems in the energy sector - three case studies from TSO business |
|---|---|
| Author / Organization | Fabian Heymann, Antoine Marot, Matthias Galus, Medha Subramanian |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2024 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-96584 |
| Original Source Link | Visit Source Page on hal.science » |