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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Vydáno v:IEEE transactions on sustainable energy Ročník 14; číslo 4; s. 1 - 15
Hlavní autoři: Zheng, Zhong, Yang, Luoxiao, Zhang, Zijun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Popis
Shrnutí: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 mixture model is adopted to better model the asymmetric and heterogeneous conditional distribution of the wind power given the wind speed. The conditional variational autoencoder is composed of an encoder and a decoder network. The encoder infers the state of the latent random variable given the wind speed and wind power, while the decoder learns the observational conditional distribution of the wind power given the wind speed and latent variable. With a well-trained conditional variational autoencoder, the conditional probability density function of the wind power could be estimated through the decoder network by sampling the latent random variable from its prior distribution. Wind turbine supervisory control and data acquisition datasets are used in experiments to validate advantages of the proposed method. Experimental results show that the proposed method outperforms other benchmarking deterministic and probabilistic wind power curve models with the lower continuous ranked probability score and more reliable and sharper prediction intervals. Experiments also reflect the better robustness of the conditional variational autoencoder to data pre-processed using univariate or multivariate inputs, as well as its superiority and potential for the wind power estimation considering multivariate inputs.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2023.3283515