Dynamic modeling of room temperature and thermodynamic efficiency for direct expansion air conditioning systems using Bayesian neural network

Abstract

In this paper, dynamic performance identification for a direct expansion (DX) air conditioning (AC) system is proposed using Bayesian artificial neural network (ANN). The input and output datasets are generated by a dedicated AC simulator by varying the compressor speed in various signal amplitudes and including dynamic cooling load and ambient temperature. The exergy destruction, which represents the work potential losses in the system and room temperature indicating the thermal comfort are selected as the output variables. The key parameters of an ANN model, including the number of neurons and tapped delay lines, are optimized to improve the prediction accuracy. The results show that the dynamic response of the exergy destruction and room temperature can be predicted accurately by the optimized ANN model using three neurons, a Bayesian regularization algorithm, five delayed inputs for the compressor speed and room temperature, and six delayed inputs for the cooling load and ambient temperature. The validation of the multi-step-ahead prediction showed satisfying results with respect to the root mean squared errors (RMSEs) and coefficient of variations (CVs) of the room temperature (RMSE: 0.18 °C and CV: 0.85%) and exergy destruction (RMSE: 1.79 W and CV: 0.4%). Accordingly, the identification of the AC system behavior presented in this paper could be further implemented to control the DX AC system operation to achieve a desired thermal comfort with low exergy destruction.