Partager

Observateurs d’état pour la commande optimale en utilisant des modèles de bâtiments de type boîte blanche.

State observers for optimal control using white-box building models.

Numéro : pap. 3245

Auteurs : CUPEIRO FIGUEROA I., DRGONA J., ABDOLLAHPOURI M., et al.

Résumé

In order to improve the energy efficiency of buildings, optimal control strategies, such as model predictive control (MPC), have proven to be potential techniques for intelligent operation of energy systems in buildings. However, in order to perform well, MPC needs an accurate controller model of the building to make correct predictions of the building thermal needs (feedforward) and the algorithm should ideally use measurement data to update the model to the actual state of the building (feedback). In this paper, a white-box approach is used to develop the controller model for an office building, leading to a model with more than 1000 states. As these states are not directly measurable, a state observer needs to be developed. In this paper, we compare three different state estimation techniques commonly applied to optimal control in buildings by applying them on a simulation model of the office building but fed with real measurement data. The considered observers are stationary Kalman Filter, time-varying Kalman Filter, and Moving Horizon Estimation. Summarizing the results, all estimators can achieve low output estimation error, but, in the case of the Kalman filters, the estimated state values are not physical. In the case of MHE, the model firstly had to be reduced to 200 states in order to evade the non-positive definite quadratic formulation present in the model and converge in tractable computation time.

Documents disponibles

Format PDF

Pages : 10

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 : State observers for optimal control using white-box building models.
  • Identifiant de la fiche : 30024752
  • Langues : Anglais
  • Sujet : Environnement
  • Source : 2018 Purdue Conferences. 5th International High Performance Buildings Conference at Purdue.
  • Date d'édition : 09/07/2018

Liens


Voir d'autres communications du même compte rendu (88)
Voir le compte rendu de la conférence

Indexation