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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| Vydané v: | IEEE transactions on sustainable energy Ročník 14; číslo 4; s. 1 - 15 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1949-3029, 1949-3037 |
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| Abstract | 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. |
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| AbstractList | 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. In this article, 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. |
| Author | Zhang, Zijun Yang, Luoxiao Zheng, Zhong |
| Author_xml | – sequence: 1 givenname: Zhong orcidid: 0000-0002-1741-0554 surname: Zheng fullname: Zheng, Zhong organization: School of Data Science, City University of Hong Kong, Kowloon, Hong Kong – sequence: 2 givenname: Luoxiao orcidid: 0000-0001-6298-5333 surname: Yang fullname: Yang, Luoxiao organization: School of Data Science, City University of Hong Kong, Kowloon, Hong Kong – sequence: 3 givenname: Zijun orcidid: 0000-0002-2717-5033 surname: Zhang fullname: Zhang, Zijun organization: School of Data Science, City University of Hong Kong, Kowloon, Hong Kong |
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| Snippet | In this paper, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. To advance the modeling... In this article, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. To advance the modeling... |
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| SubjectTerms | 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 |
| Title | Conditional Variational Autoencoder Informed Probabilistic Wind Power Curve Modeling |
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