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Approche de modélisation hybride pour une meilleure identification du modèle de réseau thermique des bâtiments et une meilleure prévision. 

Hybrid modeling approach for better identification of building thermal network model and improved prediction.

Numéro : 3489

Auteurs : HAM S. W., KIM D.

Résumé

The gray-box modeling approach (i.e., semi-physical thermal network model) has been widely used for prediction applications for buildings such as a model predictive control (MPC). However, applying the modeling approach for practical buildings is still challenging due to unmeasured disturbances such as occupants, lighting, appliances, and in/exfiltration loads. To overcome this problem, several system identification approaches have been proposed by considering the dynamics of unmeasured disturbance. However, the performance of long-term (e.g., one day) zone temperature or load predictions could still be very poor, and this is an important research topic for enabling grid-interactive buildings. In this study, we propose a hybrid modeling approach to improve long-term temperature or load predictions. Several system identification approaches for gray-box models are compared using simulations to understand the limitations. A neural network model that accounts for unmeasured disturbance is developed by considering the limitation of the graybox model and is combined with the gray-box model. This hybrid model approach shows 0.24°C root mean squared error (RMSE) for 1-day ahead temperature prediction on average, while the conventional gray-box model shows 1.1°C RMSE on average.

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Pages : 10 p.

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Détails

  • Titre original : Hybrid modeling approach for better identification of building thermal network model and improved prediction.
  • Identifiant de la fiche : 30030244
  • Langues : Anglais
  • Source : 2022 Purdue Conferences. 7th International High Performance Buildings Conference at Purdue.
  • Date d'édition : 2022
  • Document disponible en consultation à la bibliothèque du siège de l'IIF uniquement.

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