aceExplainable 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…
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.
Evidence
- Peer-reviewedScientific Reports2026-09-02
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Truvace Impact Record TRV-2026-0968, v1: “Explainable and domain-adaptive prediction models for refrigerant charging in air conditioning systems within industrial processes.” Truvace, 2026-09-03. /record/TRV-2026-0968 (accessed at citation time). sha256 c6242ebeac37e553…
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