A novel deep learning ensemble model with data denoising for short-term wind speed forecasting-Reference-Cited by-同舟云学术

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A novel deep learning ensemble model with data denoising for short-term wind speed forecasting

Published:2020-03 Issue: Volume:207 Page:112524
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Peng Zhiyun,Peng Sui,Fu Lidan,Lu Binchun,Tang Junjie^ORCID,Wang Ke,Li Wenyuan

Funder

National Natural Science Foundation of China

National Science Fund for Distinguished Young Scholars of China

Graduate Research and Innovation Foundation of Chongqing

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference63 articles.

1. Integrating large scale wind power into the electricity grid in the Northeast of Brazil;De Jong;Energy,2016

2. Wind farm efficiency by adaptive neuro-fuzzy strategy;Petkovic;Int J Electr Power Energy Syst,2016

3. Forecasting wind power – modeling periodic and non-linear effects under conditional heteroscedasticity;Ziel;Appl Energy,2016

4. Global Wind Report Annual Market Update 2017; 2017. .

5. Design and state of art of innovative wind turbine systems;Nikolic;Renew Sustain Energy Rev,2016

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