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Communication Dans Un Congrès Année : 2013

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

Résumé

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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Dates et versions

hal-00838776 , version 1 (08-07-2013)

Identifiants

  • 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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