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Identifying The Presence Of People In A Room Based On Machine Learning Techniques Using Data Of Room Control Systems

  • When monitoring the energy performance of buildings, it may be of interest to identify the occupation periods of people in the room due to their possible impact on the energy balance. In order to be able to carry out a comprehensive energy assessment of the system and building, it is necessary to be able to classify user influence during the evaluation. This thesis investigates how the presence of people in a room can be determined cost-effectively and with little additional effort. The aim is to determine which sensors of a room control system provide sufficiently reliable data. The presence of 1-2 persons was examined on a test facility of the Technical University Rosenheim. The air-, mean radiation- and surface-temperatures, the air humidity as well as the CO2 and VOC concentrations were measured. For the analysis, a method of supervised machine and statistical learning, random forest, is used. The smallest model error detected in predicting the presence of 1 or 2 persons from CO2 sensor data is 1.43%. The error rates are low for all tested models if time-dynamic effects are used as predictors and the data is processed in a so-called time period form. Additionally, the ways in which this data should ideally be made available for future measurements and processed to facilitate analysis with machine and statistical learning techniques have been investigated. A further goal is to apply the models developed on measurement series in laboratory environments to real rooms and to assess the transferability of these models.

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Metadaten
Author:Lucia Hanfstaengl, Michael Parzinger, Markus Wirnsberger, Uli Spindler, Ulrich Wellisch
URN:urn:nbn:de:bvb:861-opus4-14077
Parent Title (English):IEA EBC Annex 71: Building energy performance assessment based on in-situ measurements, 6th expert meeting, April 08.-10. 2019 - Bilbao, ES
Document Type:Conference Proceeding
Language:English
Publication Year:2019
Tag:occupancy; random forest; supervised machine and statistical learning
Page Number:7
First Page:1
Last Page:7
Project Title:Entwicklung einer Methodik zur Bestimmung der Energieeffizi-enz von Gebäuden auf Basis von optimierten in situ Messungen
Acronym:Insitu Nachweis
Funding Agency:Bundesministerium für Wirtschaft und Energie BMWi
Grant Number:03ET1509C
faculties / departments:Zentrum für Forschung, Entwicklung und Transfer