| Issue |
EPJ Web of Conferences
Volume 33, 2012
2nd European Energy Conference
|
|
|---|---|---|
| Article Number | 05009 | |
| Number of page(s) | 8 | |
| Section | End Use of Energy | |
| DOI | https://doi.org/10.1051/epjconf/20123305009 | |
| Published online | 02 October 2012 | |
https://doi.org/10.1051/epjconf/20123305009
Building lighting energy consumption modelling with hybrid neural-statistic approaches
1 University of Calabria,
2 Energy New technologies and sustainable Economic development Agency (ENEA), Casaccia R.C., Via Anguillarese 301, 00123 Rome, Italy,
a e-mail: fiorellalauro@gmail.com
b e-mail: claudia.meloni@enea.it
c e-mail: stefano.pizzuti@enea.it
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, published by EDP Sciences, 2012
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