Experimental implementation of artificial neural network for cost effective and non-intrusive performance estimation of air conditioning systems

Abstract

Owing to the high variability of operating conditions and the complexity of dynamic phenomena occurring within air conditioning cycles, the realistic performance estimation of these systems remains an open question in this field. This paper demonstrates the applicability of a cost-effective estimation method based on an artificial neural network exclusively using four refrigerant temperatures as the network input. The experimental datasets are collected from a reference experimental facility. The system is operated with variable cooling load, outdoor temperature, and indoor temperature settings, as representative of the actual operation. The artificial neural network structure was optimized by considering the effect of previous time step inputs, number of neurons, sampling time, and number of training data. The results reveal that the developed model can successfully estimate the cooling capacity of an air conditioning system during on–off, continuous unsteady, and steady operation, using four temperature inputs with relative averaged error below 5%.