Empowering the Grid: Collaborative Edge Artificial Intelligence for Decentralized Energy Systems
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909607559954432 |
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| author | Paula Jr, Eddie de Bunda, Niel Karim, Hezerul Abdul AlDahoul, Nouar Tan, Myles Joshua Toledo |
| author_facet | Paula Jr, Eddie de Bunda, Niel Karim, Hezerul Abdul AlDahoul, Nouar Tan, Myles Joshua Toledo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Empowering the Grid: Collaborative Edge Artificial Intelligence for Decentralized Energy Systems Paula Jr, Eddie de Bunda, Niel Karim, Hezerul Abdul AlDahoul, Nouar Tan, Myles Joshua Toledo Emerging Technologies 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. |
| title | Empowering the Grid: Collaborative Edge Artificial Intelligence for Decentralized Energy Systems |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2505.07170 |