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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2013-07-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/6352 |
| Summary: | 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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| ISSN: | 2283-9216 |