Conditional Variational Autoencoder Informed Probabilistic Wind Power Curve Modeling

In this paper, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. To advance the modeling performance, the latent random variable is introduced to characterize underlying weather and wind turbine conditions. The infinite Gaussian mixt...

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Veröffentlicht in:	IEEE transactions on sustainable energy Jg. 14; H. 4; S. 1 - 15
Hauptverfasser:	Zheng, Zhong, Yang, Luoxiao, Zhang, Zijun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.10.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coders Computational modeling Conditional probability Data acquisition Data models Data processing Data-driven models deep neural networks Electric power distribution Modelling Multivariate analysis Predictive models Probabilistic logic probabilistic modeling Probabilistic models Probability density functions Probability theory Random variables Statistical analysis Supervisory control and data acquisition Turbines Wind power wind power curve Wind power generation Wind speed Wind turbines
ISSN:	1949-3029, 1949-3037
Online-Zugang:	Volltext
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