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Titel:

Occupancy modeling on non-intrusive indoor environmental data through machine learning

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Banihashemi, F.; Weber, M.; Deghim, F.; Zong, C.; Lang, W.
Abstract:
The primary drivers of energy consumption within buildings are the occupants. Non-intrusive Internet of Things (IoT) technology can be utilized to detect occupancy and optimize energy performance while preserving the privacy of building occupants. This study explores the suitability of various indoor environmental data for occupancy detection in office rooms. Data was collected utilizing an IoT sensory device, recording CO2 concentration, air temperature, relative humidity, indoor air quality, s...     »
Stichworte:
NuData_Campus
Zeitschriftentitel:
Building and Environment
Jahr:
2024
Band / Volume:
254
Seitenangaben Beitrag:
111382
Volltext / DOI:
doi:10.1016/j.buildenv.2024.111382
Verlag / Institution:
Elsevier BV
E-ISSN:
0360-1323
Publikationsdatum:
01.04.2024
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