@misc{10481/77979,
year = {2016},
month = {8},
url = {https://hdl.handle.net/10481/77979},
abstract = {This paper addresses the problem of energy consumption prediction using neural networks
over a set of public buildings. Since energy consumption in the public sector comprises a substantial
share of overall consumption, the prediction of such consumption represents a decisive issue in
the achievement of energy savings. In our experiments, we use the data provided by an energy
consumption monitoring system in a compound of faculties and research centers at the University
of Granada, and provide a methodology to predict future energy consumption using nonlinear
autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs
(NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for
performing energy consumption prediction, but also that exogenous data may help to improve the
accuracy of predictions.},
organization = {Department of Computer Science and Artificial Intelligence of the University of Granada	
TIC111
TIN201564776-C3-1-R},
publisher = {MDPI},
keywords = {Energy efficiency},
keywords = {Neural networks},
keywords = {Time series prediction},
title = {An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings},
doi = {10.3390/en9090684},
author = {Baca Ruiz, Luis Gonzaga and Pegalajar Cuéllar, Manuel and Delgado Calvo-Flores, Miguel and Pegalajar Jiménez, María Del Carmen},
}