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

Author

Listed:
  • Luca Cotti

    (Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy)

  • Davide Guizzardi

    (Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy)

  • Barbara Rita Barricelli

    (Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy)

  • Daniela Fogli

    (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.

Suggested Citation

  • Luca Cotti & Davide Guizzardi & Barbara Rita Barricelli & Daniela Fogli, 2024. "Enabling End-User Development in Smart Homes: A Machine Learning-Powered Digital Twin for Energy Efficient Management," Future Internet, MDPI, vol. 16(6), pages 1-16, June.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:6:p:208-:d:1414681
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