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Open AccessArticle

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain
Author to whom correspondence should be addressed.
Academic Editors: José C. Riquelme, Alicia Troncoso and Francisco Martínez-Álvarez
Energies 2016, 9(9), 684;
Received: 14 July 2016 / Revised: 18 August 2016 / Accepted: 23 August 2016 / Published: 26 August 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
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. View Full-Text
Keywords: energy efficiency; neural networks; time series prediction energy efficiency; neural networks; time series prediction
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MDPI and ACS Style

Ruiz, L.G.B.; Cuéllar, M.P.; Calvo-Flores, M.D.; Jiménez, M.D.C.P. An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings. Energies 2016, 9, 684.

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