Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors

Parise, Alec and Manso Callejo, Miguel Ángel and Cao, Hung and Wachowicz, Monica (2021). Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors. In: "AGILE: GIScience Series, 2, 9, 2021", 8–11 June 2021, Chania, Crete, Greece. pp. 1-13. https://doi.org/10.5194/agile-giss-2-9-2021.

Description

Title: Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors
Author/s:
  • Parise, Alec
  • Manso Callejo, Miguel Ángel
  • Cao, Hung
  • Wachowicz, Monica
Item Type: Presentation at Congress or Conference (Article)
Event Title: AGILE: GIScience Series, 2, 9, 2021
Event Dates: 8–11 June 2021
Event Location: Chania, Crete, Greece
Title of Book: Proceedings of the 24th AGILE Conference on Geographic Information Science (AGILE’2021)
Date: 4 June 2021
Volume: 2
Subjects:
Freetext Keywords: Internet of Things; Occupant behavior; Non-intrusive sensing; Prophet forecasting model
Faculty: E.T.S.I. en Topografía, Geodesia y Cartografía (UPM)
Department: Ingeniería Cartográfica y Topografía
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied.

More information

Item ID: 67401
DC Identifier: https://oa.upm.es/67401/
OAI Identifier: oai:oa.upm.es:67401
DOI: 10.5194/agile-giss-2-9-2021
Official URL: https://agile-giss.copernicus.org/articles/2/9/2021/
Deposited by: Memoria Investigacion
Deposited on: 27 Jan 2022 18:35
Last Modified: 27 Jan 2022 18:41
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