Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation

RENEWABLE ENERGY

卷:86

Article

Lazrak, Amine^1,2,3  Boudehenn, Francois²  Bonnot, Sylvain²  Fraisse, Gilles³  Leconte, Antoine²  Papillon, Philippe²  Souyri, Bernard³ 

[1] ADEME, Angers, France

[2] CEA LITEN INES, Le Bourget Du Lac, France

[3] Univ Savoie, CNRS, LOCIE, Le Bourget Du Lac, France

关键词: Thermal systems;    Absorption chiller;    Performance estimation;    Dynamic modelling;    Artificial neural networks;    System testing;   

DOI  :  10.1016/j.renene.2015.09.023

来源: Elsevier

【 摘 要 】

The aim of this paper is to present a methodology to model and evaluate the energy performance and outlet temperatures of absorption chillers so that users can have reliable information on the long-term performance of their systems in the desired boundary conditions before the product is installed. Absorption chillers' behaviour could be very complex and unpredictable, especially when the boundary conditions are variable. The system dynamic must therefore be included in the model. Artificial neural networks (ANNs) have proved to be suitable for handling such complex problems, particularly when the physical phenomena inside the system are difficult to model. Reliable black box ANN modelling is able to identify the system's global model without any advanced knowledge of its internal operating principles. Knowledge of the system's global inputs and outputs is sufficient. The methodology proposed was applied to evaluate a commercial absorption chiller. Predictions of the ANN model developed were compared, with a satisfactory degree of precision, to 2 days of experimental measures. These days were chosen to be representative of the real dynamic operating conditions of an absorption chiller. The neural model predictions are very satisfactory: absolute relative errors of the transferred energy are within 0.1 6.6%. (C) 2015 Elsevier Ltd. All rights reserved.

【 授权许可】

Free