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TRUVACE RECORD VERSION
record: TRV-2026-0968
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-03T06:03:11.005183Z
status: published
lens: g_space
sector: lifestyle
headline: Explainable and domain-adaptive prediction models for refrigerant charging in air conditioning systems within industrial processes
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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). 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.
limitation: 
tag: Evidence-backed gain
key_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).
rundown: 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.
sources:
- peer_reviewed | Scientific Reports | https://doi.org/10.1038/s41598-026-55486-w | 2026-09-02
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