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Prévision de la consommation d'électricité d'un refroidisseur utilisant l'analyse des séries temporelles et les réseaux neuronaux artificiels.

Prediction of chiller power consumption using time series analysis and artificial neural networks.

Numéro : pap. 539

Auteurs : FAN C., XIAO F., WANG S.

Résumé

This paper compares the accuracy and computation times of two widely used modelling techniques, i.e. time series analysis and artificial neural network (ANN), for one-hour-ahead prediction of chiller power consumption. A linear Autoregressive Integrated Moving Average (ARIMA) model, a nonlinear Self-Excited Threshold Autoregressive (SETAR) model and two ANN models were built for the chillers installed in the tallest commercial building in Hong Kong. Clustering analysis is employed to preprocess the chiller power data prior to the building of time series models, resulting in four typical 2-day clusters from one-month of chiller power consumption data. Two ANN models are developed from the unclustered data.
The results show that the modified ANN model is the best performer, with a mean absolute percentage error (MAPE) below 5% for the out-of-sample predictions, but consumes the most computation time of over 200s compared with 10s for time series models. It was also found that clustering analysis clearly improves the performance of the time series model. The nonlinear SETAR model, however, does not perform better than the linear ARIMA model. Trade-off between prediction accuracy and computation time must be considered when selecting the appropriate prediction model of chiller power consumption in practice.

Documents disponibles

Format PDF

Pages : 11 p.

Disponible

  • Prix public

    20 €

  • Prix membre*

    15 €

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Prediction of chiller power consumption using time series analysis and artificial neural networks.
  • Identifiant de la fiche : 30009340
  • Langues : Anglais
  • Source : Clima 2013. 11th REHVA World Congress and 8th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings.
  • Date d'édition : 16/06/2013

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