Leveraging artificial neural networks for air humidity measurement in air conditioning systems

Abstract

Accurate measurement of air humidity serves as the foundation for the air conditioning system to achieve and maintain optimal indoor thermal environments. The current humidity sensors, due to their complexity, high cost, and susceptibility to contamination, are deemed unsuitable for humidity testing in the practical deployment of air conditioning systems. This underscores the need for the evolution of novel humidity measurement techniques. This study presented the development of an indirect method for measuring air relative humidity by evaluating certain readily available parameters. The underlying principle is the high degree of coupling in the heat and mass transfer occurring on the evaporator. This coupling facilitates the derivation of the relative humidity of the air at the air conditioning system's inlet from parameters such as temperature and air volume. In an effort to swiftly and accurately derive the relative humidity of the air from numerous parameters, this study employed the artificial neural networks (ANN) methodology and constructed an ANN model. Upon comparison with experimental data, it was discerned that the maximum absolute error in all prediction results was below 4.5% RH, with a substantial proportion falling below 2.5% RH. Further research corroborated that the inclusion of the degree of refrigerant superheat as a training parameter for the model exerted negligible influence on prediction accuracy. The prediction results yielded RMSE values of 0.72% and 0.71% respectively. These findings suggest that the proposed method exhibits a high degree of accuracy, thereby demonstrating its potential applicability in the field testing of air conditioning systems.