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Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems

Author

Listed:
  • Francesca Marcello

    (Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy
    These authors contributed equally to this work.)

  • Virginia Pilloni

    (Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy
    National Telecommunication Inter University Consortium (CNIT), Research Unit of Cagliari, 09123 Cagliari, Italy
    These authors contributed equally to this work.)

  • Daniele Giusto

    (Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy
    National Telecommunication Inter University Consortium (CNIT), Research Unit of Cagliari, 09123 Cagliari, Italy)

Abstract

Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of 81 % . Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about 60 % .

Suggested Citation

  • Francesca Marcello & Virginia Pilloni & Daniele Giusto, 2019. "Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems," Energies, MDPI, vol. 12(13), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2631-:d:246743
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    References listed on IDEAS

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    Cited by:

    1. Pierluigi Siano & Miadreza Shafie-khah, 2020. "Special Issue on Advanced Approaches, Business Models, and Novel Techniques for Management and Control of Smart Grids," Energies, MDPI, vol. 13(11), pages 1-3, May.
    2. Pawan Kumar & Gagandeep Singh Brar & Surjit Singh & Srete Nikolovski & Hamid Reza Baghaee & Zoran Balkić, 2019. "Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management," Energies, MDPI, vol. 12(23), pages 1-31, November.

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