Empowering the Grid: Collaborative Edge Artificial Intelligence for Decentralized Energy Systems

TL;DR

This paper explores how collaborative Edge AI techniques like federated learning can enhance decentralized energy systems by improving demand response, maintenance, and optimization while addressing privacy and scalability challenges.

Contribution

It introduces novel approaches integrating Edge AI with blockchain and adaptive architectures for decentralized energy management.

Findings

Edge AI improves real-time energy optimization

Federated learning enhances privacy in energy data

Blockchain supports secure decentralized control

Abstract

This paper examines how decentralized energy systems can be enhanced using collaborative Edge Artificial Intelligence. Decentralized grids use local renewable sources to reduce transmission losses and improve energy security. Edge AI enables real-time, privacy-preserving data processing at the network edge. Techniques such as federated learning and distributed control improve demand response, equipment maintenance, and energy optimization. The paper discusses key challenges including data privacy, scalability, and interoperability, and suggests solutions such as blockchain integration and adaptive architectures. Examples from virtual power plants and smart grids highlight the potential of these technologies. The paper calls for increased investment, policy support, and collaboration to advance sustainable energy systems.