Innovative approaches to overcome inadequate measurements in heat pumps with non-fluorinated refrigerants

• Published in: Energy conversion and management Vol. 319; p. 118970
• Main Authors: Song, Yang, Caramaschi, Matteo, Rolando, Davide, Madani, Hatef
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Data driven; Heat pump; Machine learning; Reduced-parameter; Semi-empirical model; Transfer learning; Data driven; Heat pump; Transfer learning; Semi-empirical model; Reduced-parameter; Machine learning
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2024.118970

As the transition away from fluorinated refrigerants occurs due to F-gas and PFAS regulations, heat pumps face the challenge of adapting to new non-fluorinated refrigerants. Evaluating heat pump performance during this transition is challenging due to limited operational data on the new refrigerants. Conducting long-term tests to fully understand a heat pump’s performance with all possible refrigerants is labor-intensive and economically burdensome. This study introduces two complementary reduced-parameter models to assess heat pump performance across multiple new natural refrigerants despite limited data. A transfer learning model, leveraging knowledge from existing data-rich refrigerants, has been developed to evaluate the performance of heat pumps using new, data-scarce natural refrigerants. However, due to the lack of transparency in transfer learning models, semi-empirical models are being developed in parallel. The semi-empirical models, across multiple natural refrigerants, are capable of analyzing the thermodynamics and heat transfer processes within the heat pump system by utilizing only limited easy-to-measure variables as inputs. The transfer learning model demonstrates high accuracy for all outputs across seven refrigerants with RRMSE all below 7%. In comparison, the semi-empirical models are less accurate, with RRMSE results under 25% for all parameters except compressor power. By integrating these two models, a comprehensive framework is established for assessing heat pump performance with both high accuracy and a deeper understanding of the system. •Data on 8 non-fluorinated refrigerants in heat pumps is collected and analyzed.•ANN-based transfer learning boosts accuracy for data-scarce natural refrigerants.•Semi-empirical models across multiple natural refrigerants reveal physical dynamics.•Dual-model approach to assess heat pump performance given different scenarios.