RAI4IoE: Responsible AI for Enabling the Internet of Energy
Minhui Xue, Surya Nepal, Ling Liu, Subbu Sethuvenkatraman, Xingliang, Yuan, Carsten Rudolph, Ruoxi Sun, Greg Eisenhauer

TL;DR
This paper proposes a framework for responsible and equitable AI integration in the Internet of Energy, addressing fairness, privacy, and security challenges as renewable energy resources become more interconnected and AI-driven.
Contribution
It introduces the RAI4IoE framework, focusing on equitable participation and responsible data use in AI-enabled energy systems within the Internet of Energy.
Findings
Framework for equitable AI in IoE proposed
Addresses privacy, security, and safety concerns
Supports reliable, sustainable energy services
Abstract
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…
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Taxonomy
TopicsSmart Grid Security and Resilience · IoT and Edge/Fog Computing · Blockchain Technology Applications and Security
