@inproceedings{MeckingScheibengraberKruseetal.2017, author = {Mecking, Simon and Scheibengraber, Markus and Kruse, Tobias and Schanda, Ulrich and Wellisch, Ulrich}, title = {Energiebestimmung an Brettsperrholzbauteilen im Holzmassivbau}, series = {Deutsche Gesellschaft f{\"u}r Akustik e.V. (ed.): Fortschritte der Akustik - DAGA 2017, 43. Deutsche Jahrestagung f{\"u}r Akustik, Kiel, Deutschland}, booktitle = {Deutsche Gesellschaft f{\"u}r Akustik e.V. (ed.): Fortschritte der Akustik - DAGA 2017, 43. Deutsche Jahrestagung f{\"u}r Akustik, Kiel, Deutschland}, year = {2017}, abstract = {Ein Ziel des DFG-AiF-Clusterforschungsvorhabens Vibroakustik im Planungsprozess f{\"u}r Holzbauten ist die Reduktion des schalltechnischen Planungsaufwandes bei der Holzmassivbauweise. Hierzu soll das Prognoseverfahren der DIN EN 12354 angepasst werden. Darin ist ein vereinfachter SEA-Ansatz zugrunde gelegt, welcher diffuse K{\"o}rperschallfelder in den Bauteilen sowie deren schwache Kopplung voraussetzt. Tats{\"a}chlich trifft die Annahme diffuser K{\"o}rperschallfelder nur bei Bauteilen mit geringer Ausbreitungsd{\"a}mpfung zu. Um ein Maß f{\"u}r die Kopplung der Bauteile zu erhalten, wird ein Energieverh{\"a}ltnis aus den Bauteilschnellen gebildet. In der DIN EN ISO 10848-1 wird eine Vorschrift f{\"u}r die Messung der mittleren Bauteilschnelle beschrieben. Daraus wird ein {\"o}rtlich und zeitlich gemittelter Schnellepegel je Bauteil berechnet, mit dem die Energie des Bauteils bestimmt wird. In diesem Beitrag werden Messergebnisse von K{\"o}rperschallfeldern an Brettsperrholzelementen im Hinblick auf die Energiebestimmung diskutiert. Bei der statistischen Auswertung werden die Anzahl der Anregepositionen und der Messpositionen auf dem direkt und indirekt angeregten Bauteil analysiert. In einem weiteren Schritt werden die erforderlichen Messpositionen durch eine geeignete Auswahl reduziert und mit den pauschalen, geometrischen Vorgaben der Messvorschrift aus der DIN EN ISO 10848-1 verglichen. Die Erkenntnisse werden zur Bewertung von Messergebnissen und f{\"u}r Empfehlungen an die Durchf{\"u}hrung zuk{\"u}nftiger Messungen herangezogen.}, language = {de} } @inproceedings{AntretterMayerWellisch2011, author = {Antretter, F. and Mayer, C. and Wellisch, Ulrich}, title = {An approach for a statistical model for the user behaviour regarding window ventilation in residential buildings}, series = {Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney}, booktitle = {Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney}, year = {2011}, language = {en} } @inproceedings{HanfstaenglParzingerWirnsbergeretal.2019, author = {Hanfstaengl, Lucia and Parzinger, Michael and Wirnsberger, Markus and Spindler, Uli and Wellisch, Ulrich}, title = {Identifying The Presence Of People In A Room Based On Machine Learning Techniques Using Data Of Room Control Systems}, series = {IEA EBC Annex 71: Building energy performance assessment based on in-situ measurements, 6th expert meeting, April 08.-10. 2019 - Bilbao, ES}, booktitle = {IEA EBC Annex 71: Building energy performance assessment based on in-situ measurements, 6th expert meeting, April 08.-10. 2019 - Bilbao, ES}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-14077}, pages = {1 -- 7}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{HanfstaenglParzingerSpindleretal.2020, author = {Hanfstaengl, Lucia and Parzinger, Michael and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {Identifying occupant presence in a room based on machine learning techniques by measuring indoor air conditions}, series = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, volume = {172}, booktitle = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, pages = {22005}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{ParzingerWellischHanfstaengletal.2020, author = {Parzinger, Michael and Wellisch, Ulrich and Hanfstaengl, Lucia and Sigg, Ferdinand and Wirnsberger, Markus and Spindler, Uli}, title = {Identifying faults in the building system based on model prediction and residuum analysis}, series = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, volume = {172}, booktitle = {E3S Web of Conferences, 12th Nordic Symposium on Building Physics (NSB 2020)}, pages = {22001}, year = {2020}, abstract = {The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.}, language = {en} }