@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} } @article{ParzingerHanfstaenglSiggetal.2022, author = {Parzinger, Michael and Hanfstaengl, Lucia and Sigg, Ferdinand and Spindler, Uli and Wellisch, Ulrich and Wirnsberger, Markus}, title = {Comparison of different training data sets from simulation and experimental measurement with artificial users for occupancy detection — Using machine learning methods Random Forest and LASSO}, series = {Building and Environment}, volume = {223}, journal = {Building and Environment}, doi = {10.1016/j.buildenv.2022.109313}, pages = {109313}, year = {2022}, abstract = {The applications for occupancy detection range from controlling building automation and systems, determining heat transfer coefficients and even assessing the risk of infection in rooms. Studies in the literature use various statistical models, physical models and machine learning techniques to detect occupancy. All these methods require data for training the occupancy detection models. However, data generation is time-consuming and expensive. This study demonstrates the feasibility of using simulated learning data. Using three different data sources, we tested the suitability of different methods for generating learning data. We conducted two experiments in two office spaces with a real user and an artificial user, and we generated a third data set using a building simulation model. In addition, this study compares two different machine learning approaches (Random Forest and LASSO) using environmental parameters. Both machine learning approaches could develop models with a sensitivity of at least 83 \% and a specificity of at least 97 \% with both training data sets. This work shows that it is possible to determine the presence in rooms using simulated data. The results compared to measured data were just slightly less accurate, and the added value due to the lower effort was considerable}, language = {en} } @article{NegeleOttWellisch2018, author = {Negele, Daniel and Ott, Robert and Wellisch, Ulrich}, title = {Erfolg der Privatisierung von Krankenh{\"a}usern - Eine Einsch{\"a}tzung aus der Sicht ehemaliger {\"o}ffentlicher Krankenhaustr{\"a}ger}, series = {Gesundheits{\"o}konomie \& Qualit{\"a}tsmanagement}, journal = {Gesundheits{\"o}konomie \& Qualit{\"a}tsmanagement}, number = {1}, edition = {23}, pages = {43 -- 50}, year = {2018}, abstract = {Zielsetzung In den letzten Jahren konnte eine Steigerung der Anzahl privater Krankenh{\"a}user und Betten festgestellt werden. Die Probleme der Investitionsfinanzierung deutscher Krankenh{\"a}user in Kombination mit verst{\"a}rkenden Faktoren wie der Einf{\"u}hrung der DRGs, aber auch die Erwartung einer h{\"o}heren Effizienz nach der Privatisierung einer Klinik k{\"o}nnen als Hauptgr{\"u}nde bei Privatisierungsentscheidungen genannt werden. Es soll untersucht werden, wie ehemalige {\"o}ffentliche Krankenhaustr{\"a}ger den Erfolg der Privatisierung einsch{\"a}tzen. Methodik Alle 99 identifizierten ehemaligen {\"o}ffentlichen Tr{\"a}ger, die ihr Krankenhaus in den Jahren 2003-2013 an einen privaten Eigent{\"u}mer verkauft hatten, wurden hinsichtlich ihrer Einsch{\"a}tzung von Zielen sowie Bef{\"u}rchtungen vor und nach der Privatisierung anonym und schriftlich befragt. Ergebnisse und Schlussfolgerung Zusammenfassend l{\"a}sst sich feststellen, dass bei einer ausreichenden Finanzierung der Krankenh{\"a}user aus Krankenkassenbeitr{\"a}gen (Betriebsmittel) und L{\"a}ndermittel (Investitionskosten) der Privatisierungsdruck rapide sinken w{\"u}rde. Wenn jedoch privatisiert wurde, hatte dies aus Sicht der ehemaligen Tr{\"a}ger weder einen negativen Einfluss auf die medizinische Versorgung in der Region, noch auf die Qualit{\"a}t der medizinischen Behandlung und auf den Umgang mit den Mitarbeitern. Die Einsch{\"a}tzungen der ehemaligen Tr{\"a}ger weisen eine statistisch {\"u}berpr{\"u}fte {\"U}bereinstimmung auf. Die mit einer Privatisierung verbundenen Erwartungen und Ziele werden prinzipiell als erf{\"u}llt wahrgenommen}, language = {de} }