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

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%.