Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting

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ISSN Print: 1949-243X
ISSN Online: 1947-3818
www.scirp.org/journal/epe
E-mail: epe@scirp.org

"Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting"
written by Zhongxian Men, Eugene Yee, Fue-Sang Lien, Hua Ji, Yongqian Liu,
published by Energy and Power Engineering, Vol.6 No.11, 2014

has been cited by the following article(s):

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[3]	Short-term wind speed and power forecasting using an ensemble of mixture density neural networks
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[4]	Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation
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[1]	Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction The International Journal of Electrical Engineering & Education, 2021 DOI:10.1177/0020720920988497

[2]	A review on renewable energy and electricity requirement forecasting models for smart grid and buildings Sustainable Cities and Society, 2020 DOI:10.1016/j.scs.2020.102052

[3]	Short-term wind speed and power forecasting using an ensemble of mixture density neural networks Renewable Energy, 2016 DOI:10.1016/j.renene.2015.10.014

[4]	Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation International Journal of Modern Nonlinear Theory and Application, 2015 DOI:10.4236/ijmnta.2015.42011

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