Building lighting energy consumption modelling with hybrid neural-statistic approaches

F. Lauro, C. Meloni, S. Pizzuti

Research output: Contribution to conferencePaper

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). © Owned by the authors 2012.
Original languageEnglish
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2nd European Energy Conference, E2C 2012 - , Netherlands
Duration: 1 Jan 2012 → …

Conference

Conference2nd European Energy Conference, E2C 2012
CountryNetherlands
Period1/1/12 → …

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All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

Lauro, F., Meloni, C., & Pizzuti, S. (2012). Building lighting energy consumption modelling with hybrid neural-statistic approaches. Paper presented at 2nd European Energy Conference, E2C 2012, Netherlands. https://doi.org/10.1051/epjconf/20123305009