A hybrid deep learning-based neural network for 24-h ahead wind power forecasting-Reference-Cited by-同舟云学术

A hybrid deep learning-based neural network for 24-h ahead wind power forecasting

Published:2019-09 Issue: Volume:250 Page:530-539
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Hong Ying-Yi,Rioflorido Christian Lian Paulo P.

Funder

Ministry of Science and Technology of the Republic of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference52 articles.

1. Kyoto Protocol. United Nations Framework Convention on Climate Change; 2018. [Online]. Available: http://unfccc.int/kyoto_protocol/items/2830.php.

2. Wind generation;Miller;IEEE Ind Appl Mag,2009

3. World Wind Energy Association. Wind power capacity reaches 539 GW, 52.6 GW added in 2017; 2018. [Online]. Available: https://wwindea.org/blog/2018/02/12/2017-statistics/6/.

4. Ministry of Foreign Affairs (Taiwan). Cabinet approves four-year wind power promotion plan – Taiwan Today; 2018. [Online]. Available: https://taiwantoday.tw/news.php?unit=2,6,10,15,18&post=116227.

5. A method for short-term wind power prediction with multiple observation points;Khalid;IEEE Trans Power Syst,2012

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