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pro vyhledávání: '"Pizzuti S"'
Publikováno v:
EPJ Web of Conferences, Vol 33, p 05009 (2012)
In the proposed work we aim at modelling building lighting energy consumption. We compared several classical methods to the latest Artificial Intelligence modelling technique: Artificial Neural Networks Ensembling (ANNE). Therefore, in this study we
Externí odkaz:
https://doaj.org/article/c64d517980b149bca173e5d652400135
Publikováno v:
In Energy Procedia 2014 62:411-420
Publikováno v:
In Journal of Process Control 2011 21(1):164-172
Publikováno v:
In IFAC Proceedings Volumes 2009 42(10):711-716
Akademický článek
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Autor:
Pizzuti, S. stefano.pizzuti@enea.it, Annunziato, M.1 mauro.annunziato@enea.it, Moretti, F. fabio.moretti@enea.it
Publikováno v:
Energy Efficiency (1570646X). Aug2013, Vol. 6 Issue 3, p607-616. 10p.
Autor:
Annunziato, M., Pizzuti, S. *
Publikováno v:
In International Journal of Approximate Reasoning 1999 22(1):53-71
The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of thermal comfort for office building heated by gas. Although the accuracy of the forecasting is similar for bot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2640::40bc1e9fa59eac13ef2bf20b0526bb09
http://www.scopus.com/inward/record.url?eid=2-s2.0-84906351406&partnerID=40&md5=b4d8d55d8c78e085cc38a62f49069eb9
http://www.scopus.com/inward/record.url?eid=2-s2.0-84906351406&partnerID=40&md5=b4d8d55d8c78e085cc38a62f49069eb9
In the paper a fault detection analysis through neural ensembling approaches is presented. Experimentation was carried out over two months monitoring data sets for the lighting energy consumption of an actual office building located at ENEA 'Casaccia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3668::7527676439bd6f281450a6e5fa7f4faf
https://hdl.handle.net/11590/172413
https://hdl.handle.net/11590/172413