mlsanityData to 14 Oct 2026

RAI4IoE: Responsible AI for Enabling the Internet of Energy

arXiv 2309.11691 ↗First posted 20 Sep 2023cs.AI cs.CRcited by 3

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Abstract (arXiv)

This paper plans to develop an Equitable and Responsible AI framework with enabling techniques and algorithms for the Internet of Energy (IoE), in short, RAI4IoE. The energy sector is going through substantial changes fueled by two key drivers: building a zero-carbon energy sector and the digital transformation of the energy infrastructure. We expect to see the convergence of these two drivers resulting in the IoE, where renewable distributed energy resources (DERs), such as electric cars, storage batteries, wind turbines and photovoltaics (PV), can be connected and integrated for reliable energy distribution by leveraging advanced 5G-6G networks and AI technology. This allows DER owners as prosumers to participate in the energy market and derive economic incentives. DERs are inherently asset-driven and face equitable challenges (i.e., fair, diverse and inclusive). Without equitable access, privileged individuals, groups and organizations can participate and benefit at the cost of disadvantaged groups. The real-time management of DER resources not only brings out the equity problem to the IoE, it also collects highly sensitive location, time, activity dependent data, which requires to be handled responsibly (e.g., privacy, security and safety), for AI-enhanced predictions, optimization and prioritization services, and automated management of flexible resources. The vision of our project is to ensure equitable participation of the community members and responsible use of their data in IoE so that it could reap the benefits of advances in AI to provide safe, reliable and sustainable energy services.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 76 of its 127 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

76 of 127 references.

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50 most cited of 76 All 76 (JSON)

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systemscited by 2
  2. 2024Data Poisoning and Leakage Analysis in Federated Learningcited by 1
  3. 2025Keep the Lights On, Keep the Lengths in Check: Plug-In Adversarial Detection for Time-Series LLMs in Energy Forecastingcited by 0