No CrossRef data available.
Article contents
Machine learning-based virtual sensors for reduced energy consumption in frost-free refrigerators
Published online by Cambridge University Press: 16 May 2024
Abstract
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
This study explores Machine Learning (ML) integration for household refrigerator efficiency. The ML approach allows to optimize defrost cycles, offering energy savings without complexity or cost escalation. The paper initially presents a State-of-the-Art of ML potential to improve functionality and efficiency of refrigerators. Since frost is the cause of significant energy losses, a ML-based Virtual Sensor was developed to predict frost formation on the evaporator also in low -level refrigerators. The results show the environmental significance of ML in enhancing appliance efficiency.
- Type
- Artificial Intelligence and Data-Driven Design
- Information
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
- The Author(s), 2024.
References
Anjana, H.M.K., Nimarshana, P.H.V. and Attalage, R.A. (2015), “Steady state performance variation of domestic refrigerators under different ambient conditions of Sri Lanka”, Proceedings of the 2015 Moratuwa Engineering Research Conference (MERCon), IEEE, Moratuwa, Sri Lanka, pp. 177–181, https://dx.doi.org/10.1109/MERCon.2015.7112341.CrossRefGoogle Scholar
Bansal, P., Fothergill, D. and Fernandes, R. (2010), “Thermal analysis of the defrost cycle in a domestic freezer”, International Journal of Refrigeration, Vol. 33 No. 3, pp. 589–599, https://dx.doi.org/10.1016/j.ijrefrig.2009.11.012.CrossRefGoogle Scholar
Faruque, M.W., Uddin, M.R., Salehin, S. and Ehsan, M.M. (2022), “A comprehensive thermodynamic assessment of cascade refrigeration system utilising low GWP hydrocarbon refrigerants”, International Journal of Thermofluids, Elsevier B.V., Vol. 15 No. 100177, pp. 1–15, https://dx.doi.org/10.1016/j.ijft.2022.100177.Google Scholar
Harrington, L. (2017), “Quantifying energy savings from replacement of old refrigerators”, Energy Procedia, Vol. 121, pp. 49–56, https://dx.doi.org/10.1016/j.egypro.2017.07.478.Google Scholar
Harrington, L., Aye, L. and Fuller, B. (2018a), “Energy impacts of defrosting in household refrigerators: Lessons from field and laboratory measurements”, International Journal of Refrigeration, Vol. 86, pp. 480–494, https://dx.doi.org/10.1016/j.ijrefrig.2017.12.002.CrossRefGoogle Scholar
Harrington, L., Aye, L., Fuller, B. and Hepworth, G. (2019), “Peering into the cabinet: quantifying the energy impact of door openings and food loads in household refrigerators during normal use”, International Journal of Refrigeration, Vol. 104, pp. 437–454, https://dx.doi.org/10.1016/j.ijrefrig.2019.05.040.CrossRefGoogle Scholar
Harrington, L., Aye, L. and Fuller, R.J. (2018b), “Opening the door on refrigerator energy consumption: quantifying the key drivers in the home”, Energy Efficiency, Vol. 11 No. 6, pp. 1519–1539, https://dx.doi.org/10.1007/s12053-018-9642-8.CrossRefGoogle Scholar
Hassan, H.F., Dimassi, H. and El Amin, R. (2015), “Survey and analysis of internal temperatures of Lebanese domestic refrigerators”, International Journal of Refrigeration, Vol. 50, pp. 165–171, https://dx.doi.org/10.1016/j.ijrefrig.2014.10.026.CrossRefGoogle Scholar
Hueppe, C., Geppert, J., Moenninghoff-Juessen, J., Wolff, L., Stamminger, R., Paul, A., Elsner, A., et al. (2021), “Investigating the real life energy consumption of refrigeration appliances in Germany: are present policies sufficient?”, Energy Policy, Elsevier, Vol. 155 No. 112275, https://dx.doi.org/10.1016/J.ENPOL.2021.112275.Google Scholar
International Energy Agency. (2023), “Appliances and equipment. [online]”, available at: https://www.iea.org/energy-system/buildings/appliances-and-equipment (accessed 25 October 2023).Google Scholar
James, C., Onarinde, B.A. and James, S.J. (2017), “The use and performance of household refrigerators: a review”, Comprehensive Reviews in Food Science and Food Safety, Blackwell Publishing Inc., Vol. 16 No. 1, pp. 160–179, https://dx.doi.org/10.1111/1541-4337.12242.Google Scholar
Malik, A.N., Khan, S.A. and Lazoglu, I. (2021), “A novel demand-actuated defrost approach based on the real-time thickness of frost for the energy conservation of a refrigerator”, International Journal of Refrigeration, Vol. 131, pp. 168–177, https://dx.doi.org/10.1016/j.ijrefrig.2021.07.032.CrossRefGoogle Scholar
Prayas (Energy Group). (2021), “Refrigerator electricity consumption patterns. [online]”, available at: https://energy.prayaspune.org/our-work/article-and-blog/refrigerator-electricity-consumption-patterns (accessed 30 October 2023).Google Scholar
Samsung Newsroom US. (2020), “New food AI looks inside your fridge to help you find the perfect things to cook with what you already have. [online]”, available at: https://news.samsung.com/us/new-food-ai-looks-inside-fridge-help-find-perfect-things-cook-already/ (accessed 30 October 2023).Google Scholar
Soh, Z.H.C., Ag Ku Izzul Hamzi Ag, J., Sulaiman, S.N., Abdullah, S.A.C., Ibrahim, M.N. and Aslina Abu, B. (2020), “Fridge load management system with AI and IOT alert”, Proceedings of the Annual Conference on Computer Science and Engineering Technology (AC2SET), Vol. 1088, IOP Publishing, Medan, Indonesia, pp. 1–11, https://dx.doi.org/10.1088/1757-899x/1088/1/012062.Google Scholar
SureChill. (2023), “Our technology. We harness nature. [online]”, available at: https://surechill.com/our-technology/ (accessed 25 October 2023).Google Scholar
Zaki, M.J. and Meira, W. (2014), Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, New York, https://dx.doi.org/10.1017/CBO9780511810114.CrossRefGoogle Scholar
You have
Access
Open access