Short-term prediction for nuclear power plant failure scenarios using an ensemble-based approach

Autor:	Liu, J., Vitelli, V., Seraoui, R., Francesco Di Maio, Enrico Zio
Přispěvatelé:	Laboratoire Génie Industriel - EA 2606 (LGI), CentraleSupélec, Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec (SSEC), Ecole Centrale Paris-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-CentraleSupélec-EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), EDF R&D (EDF R&D), EDF (EDF), Dipartimento di Energia [Milano] (DENG), Politecnico di Milano [Milan] (POLIMI)
Předmět:	[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] 020209 energy 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 02 engineering and technology 01 natural sciences 010305 fluids & plasmas
Zdroj:	Scopus-Elsevier Safety, Reliability and Risk Analysis ISBN: 9781138001237 Proceedings of ESREL 2013 ESREL 2013 ESREL 2013, Sep 2013, Amsterdam, Netherlands. pp.1-5 Politecnico di Milano-IRIS
Popis:	International audience; An ensemble-based approach is proposed for the short-term prediction. The proposed approach includes the selection of the inputs using Fuzzy Similarity Analysis (FSA), Probabilistic Support Vector Re-gression (SVR) model as the single model of the ensemble, and the derivation of the Prediction intervals as-sociated with the predicted value. A case study is shown, regarding the prediction of a drifting process param-eter of a Nuclear Power Plant (NPP) component.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aaf96a4b1472d16683a4166f12792531 http://www.scopus.com/inward/record.url?eid=2-s2.0-84900016535&partnerID=MN8TOARS Zobrazit plný text záznamu