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Open AccessArticle

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

by 1,†, 1,2,*,† and 1,2
1
Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy
2
National Telecommunication Inter University Consortium (CNIT), Research Unit of Cagliari, 09123 Cagliari, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2019, 12(13), 2631; https://doi.org/10.3390/en12132631
Received: 20 June 2019 / Revised: 2 July 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
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 % . View Full-Text
Keywords: activity recognition; activity detection; activity prediction; smart building; energy and comfort management activity recognition; activity detection; activity prediction; smart building; energy and comfort management
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MDPI and ACS Style

Marcello, F.; Pilloni, V.; Giusto, D. Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems. Energies 2019, 12, 2631.

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