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Machine learning-based virtual sensors for reduced energy consumption in frost-free refrigerators

Published online by Cambridge University Press:  16 May 2024

Alejandro Alcaraz ,
Dennis Ilare ,
Alessandro Mansutti  and
Gaetano Cascini
Alejandro Alcaraz
Affiliation:
Elettrotecnica ROLD, Italy
Dennis Ilare*
Affiliation:
Elettrotecnica ROLD, Italy Politecnico di Milano, Italy
Alessandro Mansutti
Affiliation:
Elettrotecnica ROLD, Italy
Gaetano Cascini
Affiliation:
Politecnico di Milano, Italy
*
dennis.ilare@polimi.it

Abstract

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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.

Keywords

artificial intelligence (AI)sustainabilitycase studyvirtual sensorsmachine learning

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 1909 - 1918
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
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.

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