An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms-Reference-Cited by-同舟云学术

An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms

Published:2023-12 Issue: Volume:284 Page:128518
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Wang Lijin,Fan Weipeng,Jiang Guoqian^ORCID,Xie Ping

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference35 articles.

1. Internet of energy – Connecting energy anywhere anytime;Vermesan,2011

2. Optimization of a 660 MWe supercritical power plant performance—A case of industry 4.0 in the data-driven operational management part 1. thermal efficiency;Muhammad Ashraf;Energies,2020

3. Optimization of a 660 MWe supercritical power plant performance—A case of industry 4.0 in the data-driven operational management. Part 2. Power generation;Muhammad Ashraf;Energies,2020

4. Gwec—global wind report 2022;Council;Glob Wind Energy Council,2021

5. Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage;Chen;Energy,2023

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