TY - CHAP A1 - Hanfstaengl, Lucia A1 - Parzinger, Michael A1 - Spindler, Uli A1 - Wellisch, Ulrich A1 - Wirnsberger, Markus T1 - Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions T2 - E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020) N2 - 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. KW - occupancy KW - maschine learning KW - random forest Y1 - 2020 UR - https://doi.org/10.1051/e3sconf/202017222005 VL - 172 SP - 22005 ER - TY - CHAP A1 - Hanfstaengl, Lucia A1 - Parzinger, Michael A1 - Wirnsberger, Markus A1 - Spindler, Uli A1 - Wellisch, Ulrich T1 - Identifying The Presence Of People In A Room Based On Machine Learning Techniques Using Data Of Room Control Systems T2 - IEA EBC Annex 71: Building energy performance assessment based on in-situ measurements, 6th expert meeting, April 08.-10. 2019 - Bilbao, ES N2 - 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. KW - occupancy KW - supervised machine and statistical learning KW - random forest Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-14077 SP - 1 EP - 7 ER -