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: This email address is being protected from spambots. You need JavaScript enabled to view it.
b e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
c e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
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, published by EDP Sciences, 2012
