Development of a Neural Network-based Building Model and Application to Geothermal Heat Pumps Predictive Control - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année :

Development of a Neural Network-based Building Model and Application to Geothermal Heat Pumps Predictive Control

(1) , (1)
1

Résumé

The use of artificial neural networks in the field of building energy management has led to remarkable results over the recent years. In this study, the development of room temperature neural network models, to be used for predictive control of geothermal heat pump systems, is discussed. The training process, including the determination of optimal input data, algorithm and structure, is detailed. The prediction performance of the developed neural network is compared to linear ARX models. Simulated data used for training and validation is generated using the TRNSYS environment. The developed model is then implemented into a predictive controller for geothermal heat pumps systems. Simulation results showed that the predictive controller can provide up to 17% energy savings in comparison with conventional controllers.
Fichier non déposé

Dates et versions

hal-01463282 , version 1 (09-02-2017)

Identifiants

  • HAL Id : hal-01463282 , version 1

Citer

Tristan Salque, Dominique Marchio. Development of a Neural Network-based Building Model and Application to Geothermal Heat Pumps Predictive Control. The Fourth International Conference on Advances in System Simulation (SIMUL 2012), Nov 2012, Lisbone, Portugal. ⟨hal-01463282⟩
80 Consultations
0 Téléchargements

Partager

Gmail Facebook Twitter LinkedIn More