EPJ Web of Conferences 33, 05009 (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

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).