Explainable and domain-adaptive prediction models for refrigerant charging in air conditioning systems within industrial processes

Accurate prediction of refrigerant deficiency in consumer air conditioning (AC) systems is critical for optimizing energy efficiency and operational stability. However, existing data-driven models often suffer from significant performance degradation due to domain shift across different AC types and a lack of explanatory transparency. To address these challenges, we propose AC-RPX (AC-Refrigerant Prediction eXplainable AI), a unified framework that integrates a Domain Encoder augmented with domain-specific tokens and a Large Language Model (LLM) adapted via Low-Rank Adaptation (LoRA). The Domain Encoder aligns sensor distributions across six consumer AC types to enable domain-robust refrigerant level prediction. Simultaneously, the LoRA-tuned LLM, trained on 280,000 expert-aligned sensor-reasoning pairs, generates case-specific chain-of-thought (CoT) explanations that explicitly link abnormal sensor patterns to the predicted refrigerant charge state. Validation on six AC sensor datasets demonstrates that AC-RPX achieves state-of-the-art accuracy and F1 scores, significantly outperforming conventional deep learning and domain adaptation baselines. Moreover, by providing intuitive natural language explanations, AC-RPX improves predictive performance over conventional rule-based heuristic methods while providing clearer diagnostic explanations on real-world consumer data. These results establish AC-RPX as a practical solution for automated refrigerant monitoring and decision support in real-world maintenance services.