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

Author

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  • Luis Gonzaga Baca Ruiz

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

  • Manuel Pegalajar Cuéllar

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

  • Miguel Delgado Calvo-Flores

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

  • María Del Carmen Pegalajar Jiménez

    (Department of Computer Science and Artificial Intelligence, University of Granada, Granada 18071, Spain)

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.

Suggested Citation

  • Luis Gonzaga Baca Ruiz & Manuel Pegalajar Cuéllar & Miguel Delgado Calvo-Flores & María Del Carmen Pegalajar Jiménez, 2016. "An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings," Energies, MDPI, vol. 9(9), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:684-:d:76787
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