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  <doc>
    <id>1675</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>22005</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>172</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions</title>
    <abstract language="eng">Knowing about the presence and number of people in a room can be of interest for precise control of heating, ventilation and air conditioning. To determine the number and presence of occupants cost-effectively, it is of interest to use already existing air condition sensors (temperature, humidity, CO2) of the building automation system. Different approaches and methods for determining presence have attracted attention in recent years. We propose an occupancy detection method based on a method of supervised machine learning. In an experiment, measurement data were recorded in a research apartment with controllable boundary conditions. The presence of people was simulated by artificial injection of water vapour, CO2 and heat dissipation. The variation of the number of artificial users, the duration of presence and the supply air volume flow of the ventilation resulted in a total of 720 combinations. By using artificial users, the boundary conditions were accurately defined, and different presence situations could be measured time-effectively. The data is evaluated with a method of supervised machine learning called random forest. The statistical model can determine precisely the number of people in over 93% of the cases in a disjoint test sample. The experiments took part in the Rosenheim Technical University of Applied Sciences laboratory.</abstract>
    <parentTitle language="eng">E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)</parentTitle>
    <identifier type="url">https://doi.org/10.1051/e3sconf/202017222005</identifier>
    <enrichment key="RS_Acronym">In Situ Nachweisverfahren</enrichment>
    <enrichment key="RS_ProjectTitle">Montag, 12. April 2021 12:14  ENOB: InsituNachweis: Entwicklung einer Methodik zur Bestimmung der Energieeffizienz von Gebäuden auf Basis von optimierten in situ Messungen. Teilvorhaben: Entwicklung eines kostengünstigen Basis-Monitoring-Ansatzes mit Beteiligung am IEA-EBC-Annex 71</enrichment>
    <enrichment key="RS_FundingAgency">Bundesministerium für Wirtschaft und Energie</enrichment>
    <enrichment key="RS_GrantNumber">03ET1509C</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Lucia Hanfstaengl</author>
    <author>Michael Parzinger</author>
    <author>Uli Spindler</author>
    <author>Ulrich Wellisch</author>
    <author>Markus Wirnsberger</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>occupancy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>maschine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>random forest</value>
    </subject>
    <collection role="ddc" number="51">Mathematik</collection>
    <collection role="ddc" number="53">Physik</collection>
    <collection role="institutes" number="">Fakultät für Angewandte Natur- und Geisteswissenschaften</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
</export-example>
