The digital landscape of the Internet of Energy (IoE) is on the brink of a
revolutionary transformation with the integration of edge Artificial
Intelligence (AI). This comprehensive review elucidates the promise and
potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a
meticulously curated research methodology, the article delves into the myriad
of edge AI techniques specifically tailored for IoE. The myriad benefits,
spanning from reduced latency and real-time analytics to the pivotal aspects of
information security, scalability, and cost-efficiency, underscore the
indispensability of edge AI in modern IoE frameworks. As the narrative
progresses, readers are acquainted with pragmatic applications and techniques,
highlighting on-device computation, secure private inference methods, and the
avant-garde paradigms of AI training on the edge. A critical analysis follows,
offering a deep dive into the present challenges including security concerns,
computational hurdles, and standardization issues. However, as the horizon of
technology ever expands, the review culminates in a forward-looking
perspective, envisaging the future symbiosis of 5G networks, federated edge AI,
deep reinforcement learning, and more, painting a vibrant panorama of what the
future beholds. For anyone vested in the domains of IoE and AI, this review
offers both a foundation and a visionary lens, bridging the present realities
with future possibilities.

数字能源互联网（IoE）的数字领域即将在集成边缘人工智能（AI）的帮助下发生革命性转变。本综述详细阐述了边缘 AI 对重塑 IoE 生态系统的承诺和潜力。通过精心策划的研究方法，文章深入探讨了专门为 IoE 量身定制的众多边缘 AI 技术。从降低延迟和实时分析到信息安全、可扩展性和成本效益等关键方面的众多好处，凸显了边缘 AI 在现代 IoE 框架中的不可或缺性。随着叙述的深入，读者将了解到实际应用和技术，重点关注设备上的计算、安全的私有推理方法以及边缘 AI 训练的前沿范式。随后进行了一项关键分析，探讨了目前面临的挑战，包括安全问题、计算难题和标准化问题。然而，随着技术的不断拓展，综述以展望未来的观点告终，设想了 5G 网络、联邦边缘 AI、深度强化学习等未来的共生关系，描绘了未来所展示的生动画面。对于在 IoE 和 AI 领域投入的任何人来说，本综述既提供了基础知识，又以一种有远见的眼光，将现实与未来的可能性联系起来。

边缘人工智能在能源互联网中的挑战与前景

Edge AI for Internet of Energy: Challenges and Perspectives

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.

该论文旨在开发一种公平和负责任的人工智能框架，其提供了用于能源互联网（IoE）的技术和算法，即 RAI4IoE。该框架利用先进的 5G-6G 网络和人工智能技术，将可再生分布式能源资源与能源网络相连接和整合，实现可靠的能源分发，并通过经济激励使分布式能源资源​​所有者能够参与能源市场。

RAI4IoE：负责任的人工智能推动能源互联网

RAI4IoE: Responsible AI for Enabling the Internet of Energy

The Internet of Energy (IoE) is a distributed paradigm that leverages smart
networks and distributed system technologies to enable decentralized energy
systems. In contrast to the traditional centralized energy systems, distributed
Energy Internet systems comprise multiple components and communication
requirements that demand innovative technologies for decentralization,
reliability, efficiency, and security. Recent advances in blockchain
architectures, smart contracts, and distributed federated learning technologies
have opened up new opportunities for realizing decentralized Energy Internet
services. In this paper, we present a comprehensive analysis and classification
of state-of-the-art solutions that employ blockchain, smart contracts, and
federated learning for the IoE domains. Specifically, we identify four
representative system models and discuss their key aspects. These models
demonstrate the diverse ways in which blockchain, smart contracts, and
federated learning can be integrated to support the main domains of IoE, namely
distributed energy trading and sharing, smart microgrid energy networks, and
electric and connected vehicle management. Furthermore, we provide a detailed
comparison of the different levels of decentralization, the advantages of
federated learning, and the benefits of using blockchain for the IoE systems.
Additionally, we identify open issues and areas for future research for
integrating federated learning and blockchain in the Internet of Energy
domains.

本文介绍了利用区块链、智能合约和联邦学习来支持分布式能源交易、智能微电网能源网络以及电动和联机车辆管理这些关键应用领域的最新技术研究，并深入探讨了去中心化、联邦学习和区块链在 IoE 系统中的优势和利益。