A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting-Reference-Cited by-同舟云学术

A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting

Published:2022-01-11 Issue: Volume: Page:
ISSN:2199-4668
Container-title:Journal of Reliable Intelligent Environments
language:en
Short-container-title:J Reliable Intell Environ

Author:

Rahman Md. Mijanur^ORCID,Shakeri Mohammad,Khatun Fatema,Tiong Sieh Kiong,Alkahtani Ammar Ahmed,Samsudin Nurul Asma,Amin Nowshad,Pasupuleti Jagadeesh,Hasan Mohammad Kamrul

Funder

LRGS grant

Publisher

Springer Science and Business Media LLC

Subject

Renewable Energy, Sustainability and the Environment,Artificial Intelligence,Computer Science Applications,Computer Networks and Communications

Link

https://link.springer.com/content/pdf/10.1007/s40860-021-00166-x.pdf

Reference97 articles.

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3. Sönnichsen, N (2020) Installed wind power capacity—worldwide 2001–2019, Statista: Energy & Environment. https://www.statista.com/statistics/268363/installed-wind-power-capacity-worldwide/. Accessed 5 Dec 2020

4. Sonnichsen N (2020) Global cumulative installed capacity of wind power by country 2019, Statista: Energy & Environment. https://www.statista.com/statistics/217522/cumulative-installed-capacity-of-wind-power-worldwide/. Accessed 5 Dec 2020

5. Wan C, Lin J, Wang J, Song Y, Dong ZY (2016) Direct quantile regression for nonparametric probabilistic forecasting of wind power generation. IEEE Trans Power Syst 32(4):2767–2778

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