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- Title
An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings.
- Authors
Baca Ruiz, Luis Gonzaga; Cuéllar, Manuel Pegalajar; Calvo-Flores, Miguel Delgado; Pegalajar Jiménez, María Del Carmen
- 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.
- Subjects
ENERGY consumption of public buildings; ARTIFICIAL neural networks; AUTOREGRESSIVE models; PREDICTION models; STATISTICAL accuracy
- Publication
Energies (19961073), 2016, Vol 9, Issue 9, p684
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en9090684