Ensemble interpolation of missing wind turbine nacelle wind speed data in wind farms based on robust particle swarm optimized generalized regression neural network-Reference-Cited by-同舟云学术

Ensemble interpolation of missing wind turbine nacelle wind speed data in wind farms based on robust particle swarm optimized generalized regression neural network

Published:2019-10-05 Issue:14 Volume:16 Page:1210-1219
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Du Jie¹²,Sun Hongchuan³,Cao Yijia⁴,Liu Yubao²⁵⁶,Pan Linlin⁶,Liu Yuewei⁶

Affiliation:

1. School of Computer & Software, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, China

2. Precision Regional Earth Modeling and Information Center, Nanjing University of Information Science & Technology, Nanjing, China

3. Key Laboratory of Transportation Meteorology, China Meteorological Administration, Jiangsu Meteorological Observatory, Nanjing, China

4. School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha, China

5. School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, China

6. National Center for Atmospheric Research, Boulder, Colorado, USA

Publisher

Informa UK Limited

Subject

Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2019.1671396

Reference30 articles.

1. RETRACTED: Artificial neural networks applications in wind energy systems: a review

2. Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study

3. An improved neural network-based approach for short-term wind speed and power forecast

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