A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN-Reference-Cited by-同舟云学术

A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN

Published:2023-11 Issue: Volume:283 Page:129139
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Meng Anbo,Zhang Haitao,Yin Hao,Xian Zikang^ORCID,Chen Shu,Zhu Zibin,Zhang Zheng,Rong Jiayu,Li Chen,Wang Chenen,Wu Zhenbo,Deng Weisi,Luo Jianqiang^ORCID,Wang Xiaolin

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Elsevier BV

Subject

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

Reference55 articles.

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4. A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network;Meng;Energy,2022

5. Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model;Zhang;Appl Energy,2019

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