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

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

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...

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Bibliographic Details
Main Authors: J. Liu, R. Seraoui, V. Vitelli, E. Zio
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2013-07-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/6352
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spelling doaj-fbecdd23436c49d6ad07681a89675c3e2021-02-21T21:06:50ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162013-07-013310.3303/CET1333145Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants EquipmentJ. LiuR. SeraouiV. VitelliE. ZioA 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.https://www.cetjournal.it/index.php/cet/article/view/6352
collection DOAJ
language English
format Article
sources DOAJ
author J. Liu
R. Seraoui
V. Vitelli
E. Zio
spellingShingle J. Liu
R. Seraoui
V. Vitelli
E. Zio
Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment
Chemical Engineering Transactions
author_facet J. Liu
R. Seraoui
V. Vitelli
E. Zio
author_sort J. Liu
title Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment
title_short Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment
title_full Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment
title_fullStr Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment
title_full_unstemmed Probabilistic Support Vector Regression for Short-Term Prediction of Power Plants Equipment
title_sort probabilistic support vector regression for short-term prediction of power plants equipment
publisher AIDIC Servizi S.r.l.
series Chemical Engineering Transactions
issn 2283-9216
publishDate 2013-07-01
description 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.
url https://www.cetjournal.it/index.php/cet/article/view/6352
work_keys_str_mv AT jliu probabilisticsupportvectorregressionforshorttermpredictionofpowerplantsequipment
AT rseraoui probabilisticsupportvectorregressionforshorttermpredictionofpowerplantsequipment
AT vvitelli probabilisticsupportvectorregressionforshorttermpredictionofpowerplantsequipment
AT ezio probabilisticsupportvectorregressionforshorttermpredictionofpowerplantsequipment
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