"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 show how we built the ANNE and a new hybrid model based on the. statistical-ANNE combination. Experimentation has been carried out over a three. months data set coming from a real office building located in the ENEA ‘Casaccia’. Research Centre. Experimental results show that the proposed hybrid statistical-ANNE approach can get a remarkable improvement with respect to the best classical method(the statistical one)."

Lauro, F., Meloni, C., Pizzuti, S. (2012). Building lighting energy consumption modelling with hybrid neural-statistic approaches. In European Energy Conference. A. McEvoy [10.1051/epjconf/20123305009].

Building lighting energy consumption modelling with hybrid neural-statistic approaches

LAURO, FIORELLA;Pizzuti, Stefano
2012-01-01

Abstract

"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 show how we built the ANNE and a new hybrid model based on the. statistical-ANNE combination. Experimentation has been carried out over a three. months data set coming from a real office building located in the ENEA ‘Casaccia’. Research Centre. Experimental results show that the proposed hybrid statistical-ANNE approach can get a remarkable improvement with respect to the best classical method(the statistical one)."
Lauro, F., Meloni, C., Pizzuti, S. (2012). Building lighting energy consumption modelling with hybrid neural-statistic approaches. In European Energy Conference. A. McEvoy [10.1051/epjconf/20123305009].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/279323
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