Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey-Reference-Cited by-同舟云学术

Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey

Published:2017-02-24 Issue:10 Volume:30 Page:3037-3048
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Ulkat Deniz,Günay M. Erdem^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-017-2895-x/fulltext.html

Reference44 articles.

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3. Kim DH, Lee GN, Kwon O (2014) Wind power prediction at southwest coast of Korea from measured wind data a. J Renew Sustain Energy 6(6):063101. doi: 10.1063/1.4897462

4. Nandha Kishore SR, Vanitha V (2013) Wind speed forecasting for grid code compliance. J Renew Sustain Energy 5(6):063125. doi: 10.1063/1.4850256

5. Zhao X, Wang S, Li T (2011) Review of evaluation criteria and main methods of wind power forecasting. Energy Procedia 12:761–769. doi: 10.1016/j.egypro.2011.10.102

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