Enabling End-User Development in Smart Homes: A Machine Learning-Powered Digital Twin for Energy Efficient Management

Author:

Cotti Luca1ORCID,Guizzardi Davide1ORCID,Barricelli Barbara Rita1ORCID,Fogli Daniela1ORCID

Affiliation:

1. Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy

Abstract

End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines to tailor the behavior of their smart homes. However, the scientific research proposed to date does not encompass methods that evaluate the suitability of user-created routines in terms of energy consumption. This paper proposes using Machine Learning to build a Digital Twin of a smart home that can predict the energy consumption of smart appliances. The Digital Twin will allow end users to simulate possible scenarios related to the creation of routines. Simulations will be used to assess the effects of the activation of appliances involved in the routines under creation and possibly modify them to save energy consumption according to the Digital Twin’s suggestions.

Funder

Italian MUR PRIN 2022 PNRR

European Union—Next Generation EU

Publisher

MDPI AG

Reference40 articles.

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