User: Guest  Login
Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
NuData_Campus
Journal title:
Building and Environment
Year:
2024
Journal volume:
254
Pages contribution:
111382
Fulltext / DOI:
doi:10.1016/j.buildenv.2024.111382
Publisher:
Elsevier BV
E-ISSN:
0360-1323
Date of publication:
01.04.2024
 BibTeX