Accurate chiller models are important for the energy conservation of chillers. The DOE-2 model is the most common chiller model. Parameter identification and error compensation are crucial for the development of an accurate DOE-2 model. However

the parameter identification of a DOE-2 model of an actual chiller is usually challenging because chillers are usually equipped with limited sensors and the quality of actual data is usually low. To address the above issues

a genetic algorithm based on an external knowledge base for parameter identification and artificial-neural-network-based (ANN-based) error compensation method are proposed. The results show that the proposed genetic algorithm can significantly reduce the computation load of parameter identification of the DOE-2 model. It can also significantly improve the accuracy of the DOE-2 model. Moreover

the accuracy of the DOE-2 model with the ANN-based error compensation is significantly higher than that of the DOE-2 model without error compensation. The MAE

RMSE

MAPE

and CV-RMSE of the model with error compensation in predicting the chilled water outlet temperature were reduced by 36.49%

46.00%

33.16%

and 45.73%

respectively

while R2 of the model with error compensation was increased by 25.75%.