A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting-Reference-Cited by-同舟云学术

A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting

Published:2024-01 Issue: Volume:286 Page:129639
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Tang Yugui,Zhang Shujing,Zhang Zhen^ORCID

Funder

Texas A and M University

Publisher

Elsevier BV

Subject

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

Reference40 articles.

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3. Wind power forecast optimization by integration of CFD and Kalman filtering;Liu;Energy Sources, Part A Recovery, Util Environ Eff,2021

4. Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models;Garcia;Adv Mech Eng,2019

5. A short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems;Huang;Energies,2018

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3. A differential privacy-preserving federated learning scheme with predictive maintenance of wind turbines based on deep learning for feature compression and anomaly detection with state assessment;Journal of Mechanical Science and Technology;2024-07