## 1 Introduction  
Greenhouse gas (GHG) emissions into the atmosphere must be reduced to mitigate global warming. In the 1990s, hydrofluorocarbons (HFCs) were adopted as zero ODP alternative refrigerants to chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), but they were listed as greenhouse gases in the Kyoto Protocol [1]. In 2014, a calendar to phase out HFCs from several sectors and applications was established under the (F-gas) Regulation (EU) No 517/2014 [2]. Among various refrigeration and air conditioning applications, commercial refrigeration systems commonly used in supermarkets contribute remarkably to GHG emissions. Higher HFC leakages in these systems are expected in these systems due to the higher refrigerant charge and probability of failure [3]. For many years, R404A and R507A have been refrigerants extended in supermarket refrigeration systems [4], but both refrigerants have very high global warming potential (GWP) values, 3943 and 3985, respectively. According to the beginning of 2020, a ban on stationary refrigeration systems using refrigerants with GWP values equal to or greater than 2500 (with some exceptions) came into effect from the EU F-gas Regulation. Therefore,manufacturers and users of supermarket refrigeration systems have tried to consider alternative refrigerants with lower GWP to replace R404A. There are different refrigerants available for this purpose, such as carbon dioxide [5], ammonia and hydrocarbons [6] and HFC mixtures and HFC/HFO mixtures [7]. One of the HFC/HFO mixtures developed and commercialized is R449A. With a lower GWP value than R404A and still non-flammable and zero-ODP fluid, this refrigerant can perform as a drop-in/light retrofit replacement in supermarket refrigeration systems [8]. It is common to analyze the performance of refrigeration systems by using conventional approaches, including analytical and experimental methods. Today, it is common to employ artificial intelligence methods to solve complicated refrigeration and air conditioning systems. An artificial neural network (ANN) can be employed [9; 10] as this method has excellent approximation capabilities. The use of ANNs makes it possible to predict the desired output even in complex refrigeration and air conditioning systems. Its simplicity, speed, and ability to model a multivariable system are the main advantages of using ANNs [11; 12]. The ANN method was employed by [PERSON] and [PERSON] [13] to predict the energy parameters in a cascade vapour compression refrigeration system using R134a. Their results revealed that the ANN had great potential to model the performance of the system reliably. [PERSON] et al. [14] developed an ANN model to study the performance of a refrigeration system. Using this technique, they proposed a refrigeration system control method for saving energy when working in part-load conditions. [PERSON] [15] employed ANN modelling to analyze the energy consumption and the thermodynamic aspects of a refrigeration system. The modelling results were in excellent agreement with the actual data. [PERSON] et al. [16] developed an ANN model to design a control strategy for indoor air temperature and humidity in a direct expansion air conditioning system. They employed 169 sets of experimental data and modelled the performance of the system. [PERSON] et al. [17] developed an ANN model to investigate the energy performance of a small refrigeration system using R134a, R450A and R513A. The ANN model predicted the system's performance well and helped to compare the system's performance using R450A and R513A with R134a for a replacement. For this transition to low GWP refrigerants, only a few studies have proposed and validated an ANN model as a tool to evaluate the energy performance and the suitability of the new alternative. Moreover, there is a lack of studies that consider field test data for feeding the ANN model despite its potential for predicting the operation and possible failures.