@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} } @article{ParzingerHanfstaenglSiggetal.2020, author = {Parzinger, Michael and Hanfstaengl, Lucia and Sigg, Ferdinand and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building's Heating, Ventilation and Air Conditioning Systems}, series = {Sustainability}, volume = {12}, journal = {Sustainability}, number = {17}, pages = {6758}, year = {2020}, abstract = {Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge.}, 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} } @techreport{SandorSchalkSussmannetal.2016, author = {Sandor, V. and Schalk, D. and Sussmann, M. and Wellisch, Ulrich}, title = {Statistische Datenanalyse und Modellierung - Einsatzm{\"o}glichkeiten von Machine Learning Verfahren. Forschungsbericht 2016 Hochschule Rosenheim}, year = {2016}, language = {de} } @article{FreySchoenfelderWellisch2016, author = {Frey, C. and Sch{\"o}nfelder, F. and Wellisch, Ulrich}, title = {Anwendung von maschinellem Lernen in der Tarifierung}, series = {Der Aktuar}, journal = {Der Aktuar}, year = {2016}, 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} } @article{SteigerWellischHellwig2010, author = {Steiger, S. and Wellisch, Ulrich and Hellwig, R. T.}, title = {Untersuchung der Eignung verschiedener Fassaden f{\"u}r automatisierte Fensterl{\"u}ftung in Schulen mit einem Entscheidungsbaumverfahren}, series = {Bauphysik}, journal = {Bauphysik}, year = {2010}, abstract = {Sollen Klassenr{\"a}ume {\"u}ber Fenster bel{\"u}ftet werden, kann dies {\"u}ber Stoßl{\"u}ftung oder genau dosierte Dauerl{\"u}ftung erfolgen. Messungen verschiedener Institutionen zeigen, dass die erforderliche regelm{\"a}ßige Stoßl{\"u}ftung in Schulen nicht praktiziert wird. Damit eine Dauerl{\"u}ftung aber funktionieren kann, muss ermittelt werden, welcher Fenstertyp bei welcher {\"O}ffnungsweite bei behaglichen thermischen Verh{\"a}ltnissen im Raum den notwendigen Luftwechsel gew{\"a}hrleistet. Deshalb wurden an einem Freilandversuchsstand des Fraunhofer-Instituts f{\"u}r Bauphysik Schwingfl{\"u}gel und Kippfl{\"u}gel auf ihre Eignung zur Dauerl{\"u}ftung in Klassenr{\"a}umen untersucht. Da sich bei den vielen Einflussfaktoren auf die freie L{\"u}ftung die deskriptive Analyse oft als sehr schwierig erweist, wird ein besonderes statistisches Verfahren, ein Entscheidungsbaumverfahren verwendet, das sowohl hinsichtlich der Auswahl der Einflussgr{\"o}ßen und der Modell{\"u}beranpassung sehr g{\"u}nstige statistischmethodische Eigenschaften besitzt. Mit diesem Verfahren k{\"o}nnen Einflussgr{\"o}ßen mit unterschiedlichen Skalenniveaus ad{\"a}quat in die Auswertung einbezogen werden. Dabei erh{\"a}lt man ein intuitiv interpretierbares Regelwerk, welches dem Anwender einen erkl{\"a}renden Einblick in die Zusammenhangsstruktur des untersuchten Prozesses liefert.}, language = {de} } @phdthesis{Wellisch1999, author = {Wellisch, Ulrich}, title = {Asymptotic behaviour of solutions of estimating equations with functional parameter}, school = {Technische Hochschule Rosenheim}, year = {1999}, language = {en} } @techreport{PruschkaWellisch1997, author = {Pruschka, H. and Wellisch, Ulrich}, title = {Asymptotic behaviour of estimation equations with functional nuisance or working parameter.}, year = {1997}, abstract = {We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing with estimation equations which have a parametric component of interest and a functional (nonparametric) nuisance component. We give sufficient conditions for the existence and the asymptotic normality of a consistent estimation equation estimator for the parameter of interest. These conditions concern the asymptotic distribution of the estimation function and of its derivative as well as the effect of the functional nuisance part in the estimation equation. In order to treat the nonparametric component we introduce a general differential calculus and a general mean value theorem. For the nonparametric part in the estimation equation we distinguish two cases: the situation of a (classical) nuisance parameter and the case of a so called working parameter. As a special case we get regularity conditions for estimation equations with finite dimensional nuisance or working parameter. As an example we present the semiparametric linear regression model.}, language = {en} } @techreport{KuechenhoffWellisch1997, author = {K{\"u}chenhoff, H. and Wellisch, Ulrich}, title = {Asymptotics for generalized linear segmented regression models with an unknown breakpoint}, year = {1997}, abstract = {We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model with an unknown breakpoint. A proof for the asymptotic normality is given. The methods are based on the work of Huber (1967). The main problem is the non--differentiability of the likelihood and the score function, which requires non--standard methods. An example from epidemiology is presented, where confidence intervals for the parameters are calculated with the asymptotic results.}, language = {en} }