Predicting the Refrigerant Amounts Across Air Conditioners With a Domain Adaptive Lightweight Transformer

Abstract

Air conditioner consumers expect their products to work well without any problem. Consumers encounter problems if the amount of refrigerant in the air conditioner is insufficient. Therefore, we propose a novel deep learning approach that predicts the amount of refrigerant in advance. Our approach differs from others, as it is not limited to specific types of air conditioners and is applicable to all types. We propose a domain adaptive Transformer (DAT) that determines the amount of refrigerant using a Transformer encoder and domain adversarial training of neural networks. The proposed DAT reduces the model parameters by using a modified lightweight Transformer encoder and adds a domain classifier to build a universal model that is not limited to a specific type of air conditioner. We conduct the experiments by collecting data on the amount of refrigerant operating in the real fields. As a result, the proposed DAT achieves higher prediction performance. It is also significant that the results predicted by DAT are more accurate than those predicted by experts dealing with air conditioner refrigerant problems. As a result, DAT-based air conditioners are commercialized in real life. Hence, consumers can prevent the shortage of refrigerant without experts.