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 token…

By the Truvace deskSeptember 3, 20261 min read1 sourcePeer-reviewedTRV-2026-0968· cite & verify

In brief

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). 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.

Main points

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 gain

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

The rundown

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.