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

Abstract : 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.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal-mines-paristech.archives-ouvertes.fr/hal-01463282
Contributeur : Joelle Andrianarijaona <>
Soumis le : jeudi 9 février 2017 - 14:41:51
Dernière modification le : lundi 12 novembre 2018 - 11:01:01

Identifiants

  • HAL Id : hal-01463282, version 1

Collections

Citation

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⟩

Partager

Métriques

Consultations de la notice

100