Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment

Abstract : A short-term forecasting approach is proposed for the purposes of condition monitoring. The proposed approach builds on the Probabilistic Support Vector Regression (PSVR) method. The tuning of the PSVR hyerparameters, the model identification and the uncertainty analysis are conducted via novel and innovative strategies. A case study is shown, regarding the prediction of a drifting process parameter of a Nuclear Power Plant (NPP) component.
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Conference papers
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https://hal-supelec.archives-ouvertes.fr/hal-00838776
Contributor : Yanfu Li <>
Submitted on : Monday, July 8, 2013 - 1:43:27 PM
Last modification on : Tuesday, May 8, 2018 - 10:22:46 AM
Long-term archiving on : Wednesday, October 9, 2013 - 4:19:45 AM

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  • HAL Id : hal-00838776, version 1

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Jie Liu, Redouane Seraoui, Valeria Vitelli, Enrico Zio. Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment. Prognostics and System Health Management Conference - PHM-2013, Sep 2013, Milano, Italy. pp.1-6. ⟨hal-00838776⟩

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