A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting-Reference-Cited by-同舟云学术

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A gated recurrent unit neural networks based wind speed error correction model for short-term wind power forecasting

Published:2019-11 Issue: Volume:365 Page:54-61
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Ding Min^ORCID,Zhou Hao,Xie Hua,Wu Min^ORCID,Nakanishi Yosuke,Yokoyama Ryuichi

Funder

National Natural Science Foundation of China

Hubei Provincial Natural Science Foundation

111 project

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference44 articles.

1. Global Wind Energy Council, Global wind statistics 2017, 2018, (http://gwec.net/about-winds/about-gwec).

2. Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor;Shen;Automatica,2019

3. Assessing the effect of wind power peaking characteristics on the maximum penetration level of wind power;Jiang;IET Gener. Transm. Distrib.,2015

4. State Grid Corporation of China, Function specification of wind power forecasting, 2016, Q/GDW 10588-2015.

5. Soft computing applications for renewable energy and energy efficiency;Cascales,2014

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