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MACHINE LEARNING-BASED VIRTUAL SENSORS FOR GUIDING USER BEHAVIOUR: A CASE STUDY ON HOUSEHOLD APPLIANCES

Published online by Cambridge University Press:  19 June 2023

Dennis Ilare ,
Gaetano Cascini ,
Stefano Manzoni  and
Alessandro Mansutti
Dennis Ilare*
Affiliation:
Politecnico di Milano; ROLD
Gaetano Cascini
Affiliation:
Politecnico di Milano;
Stefano Manzoni
Affiliation:
Politecnico di Milano;
Alessandro Mansutti
Affiliation:
ROLD
*
Ilare, Dennis, Politecnico di Milano, Italy, [email protected]

Abstract

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The Agenda 2030 calls for collective awareness, starting with individuals. The interaction between users and household appliances produces a relevant amount of data that can be elaborated through Machine Learning algorithms to guide users towards sustainable behaviours. In particular, the data already available on household appliances can be conveniently used to create Virtual Sensors, increasing the overall information about the system. This paper focuses on the description of the pipeline for the creation of Virtual Sensors and applies it to a no-frost refrigerator. The Data Acquisition phase is described and feeds the Model Creation phase. For the case study, the data have been discretized and labelled to train a Random Forest algorithm. The validation of the model has been done on an independent dataset. An analysis of the minimum prediction accuracy required for the model is reported. Furthermore, experimental data shows the effect of hot load positioning on the compressor's working time rate.

Keywords

Virtual SensorsSustainabilityMachine learningArtificial intelligenceCase study
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 2495 - 2504
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), 2023. Published by Cambridge University Press

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