TY - JOUR A1 - Parzinger, Michael A1 - Hanfstaengl, Lucia A1 - Sigg, Ferdinand A1 - Spindler, Uli A1 - Wellisch, Ulrich A1 - Wirnsberger, Markus T1 - Residual Analysis of Predictive Modelling Data for Automated Fault Detection in Building’s Heating, Ventilation and Air Conditioning Systems JF - Sustainability N2 - 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. KW - fault detection KW - HVAC KW - residual analysis Y1 - 2020 UR - https://doi.org/10.3390/su12176758 VL - 12 IS - 17 SP - 6758 ER - TY - JOUR A1 - Parzinger, Michael A1 - Hanfstaengl, Lucia A1 - Sigg, Ferdinand A1 - Spindler, Uli A1 - Wellisch, Ulrich A1 - Wirnsberger, Markus T1 - 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 JF - Building and Environment N2 - 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 KW - Simulated learning data KW - Occupancy detection KW - Random Forest KW - LASSO Y1 - 2022 U6 - https://doi.org/10.1016/j.buildenv.2022.109313 VL - 223 SP - 109313 ER - TY - JOUR A1 - Negele, Daniel A1 - Ott, Robert A1 - Wellisch, Ulrich T1 - Erfolg der Privatisierung von Krankenhäusern – Eine Einschätzung aus der Sicht ehemaliger öffentlicher Krankenhausträger T1 - Success of hospital privatisations – an assessment from the point of view of former public hospital owners JF - Gesundheitsökonomie & Qualitätsmanagement N2 - Zielsetzung In den letzten Jahren konnte eine Steigerung der Anzahl privater Krankenhäuser und Betten festgestellt werden. Die Probleme der Investitionsfinanzierung deutscher Krankenhäuser in Kombination mit verstärkenden Faktoren wie der Einführung der DRGs, aber auch die Erwartung einer höheren Effizienz nach der Privatisierung einer Klinik können als Hauptgründe bei Privatisierungsentscheidungen genannt werden. Es soll untersucht werden, wie ehemalige öffentliche Krankenhausträger den Erfolg der Privatisierung einschätzen. Methodik Alle 99 identifizierten ehemaligen öffentlichen Träger, die ihr Krankenhaus in den Jahren 2003–2013 an einen privaten Eigentümer verkauft hatten, wurden hinsichtlich ihrer Einschätzung von Zielen sowie Befürchtungen vor und nach der Privatisierung anonym und schriftlich befragt. Ergebnisse und Schlussfolgerung Zusammenfassend lässt sich feststellen, dass bei einer ausreichenden Finanzierung der Krankenhäuser aus Krankenkassenbeiträgen (Betriebsmittel) und Ländermittel (Investitionskosten) der Privatisierungsdruck rapide sinken würde. Wenn jedoch privatisiert wurde, hatte dies aus Sicht der ehemaligen Träger weder einen negativen Einfluss auf die medizinische Versorgung in der Region, noch auf die Qualität der medizinischen Behandlung und auf den Umgang mit den Mitarbeitern. Die Einschätzungen der ehemaligen Träger weisen eine statistisch überprüfte Übereinstimmung auf. Die mit einer Privatisierung verbundenen Erwartungen und Ziele werden prinzipiell als erfüllt wahrgenommen N2 - Aim In recent years, an increase in the number of private hospitals and beds could be determined. The problems of financing investments of German hospitals in combination with other reinforcing factors such as the introduction of DRGs, but also assumptions to higher efficiency after privatisation of a hospital can be identified as the main reasons for privatisation decisions. It should be examined how former public hospital operators assess the success of the privatisation. Method All 99 identified former public owners who had sold their hospitals during the years 2003 to 2013 to a private owner were interviewed anonymously and in written form with regard to their assessment of goals and fears before and after privatisation. Results and Conclusion In summary it can be said that with sufficient funding of hospitals from health insurance contributions (resources) and state funds (investment costs) privatisation pressure would fall sharply. But if privatized once, there was - from the view of the former public owners - neither a negative impact on medical care in the region, nor on the quality of medical treatment and the treatment of employees. The former public owners show a fair agreement on their assesments. The privatisations answer the expectations and the goals of the former public owners. KW - Privatisierung von Krankenhäusern KW - Gesundheitspolitik KW - Krankenhausfinanzierung KW - hospital privatisation KW - health policy KW - hospital financing Y1 - 2018 UR - https://doi.org/10.1055/s-0043-110761 IS - 1 SP - 43 EP - 50 ET - 23 ER -