The Role of Machine Learning in Optimizing the Use of Low-GWP Refrigerants in HVAC Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P119Keywords:
HVAC, Low-GWP Refrigerants, Machine Learning, Artificial Intelligence, Energy Optimization, Predictive Maintenance, Smart BuildingsAbstract
HVAC industry is among the largest energy-consumers and amount of emissions to the atmosphere worldwide. The traditional refrigerants like hydrofluorocarbons (HFCs) have a high global warming potential (GWP) and so the whole world has shifted to environmentsally benign low-global warming potential refrigerants like hydrofluoroolefins (HFOS), natural refrigerants (CO 2, ammonia, hydrocarbons as well as mixtures). Introduction of these refrigerants, however, poses new challenges on the aspects of performance of the systems, safety, optimization of efficiency and reliability in operations. Artificial intelligence (AI) and machine learning (ML) are two powerful technologies in the recent-years that have arisen to enhance the intelligence of HVAC systems to ensure predictive maintenance, adaptive control, fault detection, and energy optimization. The paper explores machine learning applications in HVAC optimization to use low-GWP refrigerants. In the study, the literature survey is thoroughly conducted, technical issues are defined, and an intelligent framework of how to monitor, optimize the performance, and achieve energy-efficient control is proposed based on the extremely intelligent data. It is suggested to use a hybrid ML method to optimize the charge of refrigerants, compressor speed, evaporator temperature, and airflow rate in real-time through a combination of a deep learning, reinforcement learning, and ensemble regression model. Simulation and experimental findings prove the great enhancement of coefficient of performance (COP), the energy consumption decrease and the increase in the accuracy of fault detection. The results affirm that machine learning is a disruptive facilitator of sustainable HVAC system and will be strategically instrumental in hastening the world to climate-friendly refrigeration solution.
References
[1] Calm, J. M. (2008). The next generation of refrigerants–Historical review, considerations, and outlook. international Journal of Refrigeration, 31(7), 1123-1133.
[2] Lorentzen, G. (1994). Revival of carbon dioxide as a refrigerant. International journal of refrigeration, 17(5), 292-301.
[3] Mota-Babiloni, A., Navarro-Esbrí, J., Makhnatch, P., & Molés, F. (2017). Refrigerant R32 as lower GWP working fluid in residential air conditioning systems in Europe and the USA. Renewable and Sustainable Energy Reviews, 80, 1031-1042.
[4] Bolaji, B. O., & Huan, Z. (2013). Ozone depletion and global warming: Case for the use of natural refrigerant–a review. Renewable and Sustainable energy reviews, 18, 49-54.
[5] Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586-3592.
[6] Yao, Y., Lian, Z., Liu, S., & Hou, Z. (2004). Hourly cooling load prediction by a combined forecasting model based on analytic hierarchy process. International journal of thermal sciences, 43(11), 1107-1118.
[7] Li, K., Su, H., & Chu, J. (2011). Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study. Energy and Buildings, 43(10), 2893-2899.
[8] Mulumba, T., Afshari, A., Yan, K., Shen, W., & Norford, L. K. (2015). Robust model-based fault diagnosis for air handling units. Energy and Buildings, 86, 698-707.
[9] Yan, K., Shen, W., Mulumba, T., & Afshari, A. (2014). ARX model based fault detection and diagnosis for chillers using support vector machines. Energy and Buildings, 81, 287-295.
[10] Tun, W., Wong, J. K. W., & Ling, S. H. (2021). Hybrid random forest and support vector machine modeling for HVAC fault detection and diagnosis. Sensors, 21(24), 8163.
[11] Wei, T., Wang, Y., & Zhu, Q. (2017, June). Deep reinforcement learning for building HVAC control. In Proceedings of the 54th annual design automation conference 2017 (pp. 1-6).
[12] Afram, A., & Janabi-Sharifi, F. (2014). Theory and applications of HVAC control systems–A review of model predictive control (MPC). Building and environment, 72, 343-355.
[13] Borda, D., Bergagio, M., Amerio, M., Masoero, M. C., Borchiellini, R., & Papurello, D. (2023). Development of anomaly detectors for HVAC systems using machine learning. Processes, 11(2), 535.
[14] Kim, B., Lee, D., Lee, S. H., & Kim, Y. (2020). Performance assessment of optimized heat pump water heaters using low-GWP refrigerants for high-and low-temperature applications. Applied Thermal Engineering, 181, 115954.
[15] Domanski, P. A., Brignoli, R., Brown, J. S., Kazakov, A. F., & McLinden, M. O. (2017). Low-GWP refrigerants for medium and high-pressure applications. International Journal of Refrigeration, 84, 198-209.
[16] Aghili, S. A., Haji Mohammad Rezaei, A., Tafazzoli, M., Khanzadi, M., & Rahbar, M. (2025). Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review. Buildings (2075-5309), 15(7).
[17] Carli, R., Cavone, G., Ben Othman, S., & Dotoli, M. (2020). IoT based architecture for model predictive control of HVAC systems in smart buildings. Sensors, 20(3), 781.
[18] Fan, C., Sun, Y., Zhao, Y., Song, M., & Wang, J. (2019). Deep learning-based feature engineering methods for improved building energy prediction. Applied energy, 240, 35-45.
[19] Chen, Y., Norford, L. K., Samuelson, H. W., & Malkawi, A. (2018). Optimal control of HVAC and window systems for natural ventilation through reinforcement learning. Energy and Buildings, 169, 195-205.
[20] Zappone, A., Di Renzo, M., & Debbah, M. (2019). Wireless networks design in the era of deep learning: Model-based, AI-based, or both?. IEEE Transactions on Communications, 67(10), 7331-7376.
[21] Patel, Z., Senjaliya, N., & Tejani, A. (2019). AI-enhanced optimization of heat pump sizing and design for specific applications. International Journal of Mechanical Engineering and Technology (IJMET), 10(11), 447-460.
[22] Tejani, A. (2021). Integrating energy-efficient HVAC systems into historical buildings: Challenges and solutions for balancing preservation and modernization. ESP Journal of Engineering & Technology Advancements, 1(1), 83-97.
[23] Tejani, A., Yadav, J., Toshniwal, V., & Gajjar, H. (2022). Achieving net-zero energy buildings: The strategic role of HVAC systems in design and implementation. ESP Journal of Engineering & Technology Advancements, 2(1), 39-55.










