Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data-Reference-Cited by-同舟云学术

Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data

Published:2019-05-31 Issue: Volume: Page:189-196
ISSN:2191-5644
Container-title:Model Validation and Uncertainty Quantification, Volume 3
language:
Short-container-title:

Author:

Mylonas C.,Abdallah I.,Chatzi E. N.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-12075-7_21

Reference7 articles.

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2. Hao, Y., Lackner, M.A., Keck, R.-E., Lee, S., Churchfield, M.J., Moriarty, P.J.: Implementing the dynamic wake meandering model in the NWTC design codes. In: 32nd ASME Wind Energy Symposium, p. 1089 (2014)

3. Turbines, W.: Part 1: design requirements. International Standard IEC 61400-1 (2005)

4. Kingma, D.P., Welling, M.: Auto-encoding variational Bayes (2013, preprint). arXiv:1312.6114

5. Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models (2014, preprint). arXiv:1401.4082

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