A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps

Abstract

Improper refrigerant charge amount (RCA) is a recurring fault in electric heat pump (EHP) systems. Because EHP systems show their best performance at optimum charge, predicting the RCA is important. There has been considerable development of data-driven techniques for predicting RCA; however, the current data-driven approaches for estimating RCA suffer from poor generalization and overfitting. This study presents a hybrid deep neural network (DNN) model that combines both a basic DNN model and a thermodynamic model to counter the abovementioned challenges of existing data-driven approaches. The data for designing models were collected from two EHP systems with different specifications, which were used for the training and testing of models. In addition to the data obtained using the basic DNN model, the hybrid DNN model uses the thermodynamic properties as a thermodynamic model. The testing results show that the hybrid DNN model has a prediction performance of 93%, which is 21% higher than that of the basic DNN model. Furthermore, for model training and model testing, the hybrid DNN model has a 6% prediction performance difference, indicating its reliable generalization capabilities. To summarize, the hybrid DNN model improves data-driven approaches and can be used for designing efficient and energy-saving EHP systems.Sustainability 2020, 12, 2914 2 of 23 and decreased the capacity by~20%; moreover, improper RCA can further reduce the efficiency of on-site ACs by 10-20% [9].In a field study, the refrigerant was improperly charged for~50% of the on-site heating, ventilating, and air-conditioning (HVAC) systems [8,10]. Furthermore, more than half of the residential cooling systems showed improper RCA problems [8]. For the long-term operation of systems, mechanical wear or improper maintenance could lead to refrigerant leakage or overcharge, which, in turn, resulted in reduced system operation efficacy and increased energy consumption [11]. Moreover, improper RCA can result in decreased system performance, increased energy consumption, and reduced life span of the system [12]. In the long run, from an economic point of view, improper RCA can lead to an increase in the operational cost of a building system; thus, if the RCA of a running system in a building can be effectively estimated, it can be positively used to solve the abovementioned problems.The topic of RCA detection is not widely discussed in the literature and only a few studies have been conducted for detecting RCA [13][14][15][16][17]. For example, a polynomial expression-based RCA detection algorithm was developed using only subcooling. The results showed relatively good predictions within a relative deviation of 8.0% [13]; however, although approaches based on mathematical expression models allow accurate predictions of RCA, they are often designed for individual systems, thus making their applications on other on-site EHP systems difficult [2,14]. Moreover, they may require several sensors to implement, thus leading to increase in cost. Other studies r...

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“…Therefore, to adapt the algorithm to other utilities, RES and climates, future research works can employ transfer learning methods. [71][72][73][74][75] Moreover, future studies in this field may also consider using deep reinforcement learning-based approach 76 and adapt it to individual households. Another limitation is that the failure of the PV system will cause the algorithm to switch to the grid indefinitely.…”

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