Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder-Reference-Cited by-同舟云学术

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Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder

Published:2021-02 Issue: Volume:164 Page:642-655
ISSN:0960-1481
Container-title:Renewable Energy
language:en
Short-container-title:Renewable Energy

Author:

Wang Lin,Tao Rui,Hu Huanling,Zeng Yu-Rong

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference33 articles.

1. Current status and future advances for wind speed and power forecasting[J];Jung;Renew. Sustain. Energy Rev.,2014

2. A hybrid wind power forecasting model based on data mining and wavelets analysis [J];Azimi;Energy Convers. Manag.,2016

3. A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting [J];Hao;Appl. Energy,2019

4. Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output[J];Cassola;Appl. Energy,2012

5. Using weather ensemble predictions in electricity demand forecasting[J];Taylor;Int. J. Forecast.,2003

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