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In dieser Arbeit wurden unterschiedliche Regler für Wohnraumlüftungsanlagen untersucht. Als Bewertungsgrößen wurden die Lüftungswärmeverluste und die kumulierte Grenzwertüberschreitung für CO2 und relative Luftfeuchte verwendet. Dafür wurde in Matlab/Simulink ein Multizonen-Simulationsmodell erstellt. Untersucht wurde ein zentrales, ein dezentrales alternierendes (Pendellüftung) sowie ein nichtalternierendes (Einzelraumlüftung) Lüftungssystem. Jedes der Lüftungssysteme wurde mit konstantem Volumenstrom, einem PI- und einem Fuzzy-Regler (Ausnahme: Pendellüftung) simuliert. Gegenüber einem Lüftungssystem mit konstantem Volumenstrom, kann durch Regelung bei mindestens gleicher Luftqualität der Lüftungswärmeverlust um mindestens ein Drittel reduziert werden.
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.
Background
Joint contractures in frail older people are associated with serious restrictions in participation. We developed the Participation Enabling CAre in Nursing (PECAN) intervention, a complex intervention to enable nurses to promote participation in nursing home residents with joint contractures. The aim of this study was to examine the feasibility of the implementation strategy and to identify enablers and barriers for a successful implementation.
Methods
The implementation of PECAN was investigated in a 6-month pilot cluster-randomised controlled trial (c-RCT). As a key component of the implementation strategy, nominated nurses were trained as facilitators in a one-day workshop and supported by peer-mentoring (visit, telephone counselling). A mixed-methods approach was conducted in conjunction with the pilot trial and guided by a framework for process evaluations of c-RCTs. Data were collected using standardised questionnaires (nursing staff), documentation forms, problem-centred qualitative interviews (facilitators, therapists, social workers, relatives, peer-mentors), and a group discussion (facilitators). A set of predefined criteria on the nursing home level was examined. Quantitative data were analysed using descriptive statistics. Qualitative data were analysed using directed content analysis.
Results
Seven nursing homes (n = 4 intervention groups, n = 3 control groups) in two regions of Germany took part in the study. Facilitators responded well to the qualification measures (workshop participation: 14/14; workshop rating: “good”; peer-mentor visit participation: 10/14). The usage of peer-mentoring via telephone varied (one to seven contacts per nursing home). Our implementation strategy was not successful in connection with supplying the intervention to all the nurses. The clear commitment of the entire nursing home and the respect for the expertise of different healthcare professionals were emphasised as enablers, whereas a lack of impact on organisational conditions and routines and a lack of time and staff competence were mentioned as barriers.
Conclusion
The PECAN intervention was delivered as planned to the facilitators but was unable to produce comprehensive changes in the nursing homes and subsequently for the residents. Strategies to systematically include the management and the nursing team from the beginning are needed to support the facilitators during implementation in the main trial.
Trial registration
German clinical trials register, DRKS00010037. Registered 12 February 2016.
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.
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.
Freifeldcharakterisierung von PV-Anlagen im Rahmen des Projektes PV- FeldLab -ein Statusbericht-
(2020)
Das Projekt PV-FeldLab verfolgt das Ziel, ein umfassendes Konzept für eine messtechnisch sehr präzise elektrische Freifeld-Charakterisierung von PV-Generatoren zu entwickeln. Es beinhaltet eine optische Untersuchung mittels drohnengestützter Thermografie und eine genaue elektrische Charakterisierung von PV-Anlagen im Freifeld, auch bei räumlicher Inhomogenität von Bestrahlungsstärke, Temperatur und bei variablen Aufbauwinkeln von Modulen im Strang. Die Verknüpfung der elektrischen und optischen Messdaten erlaubt eine effiziente Untersuchung von PV-Anlagen und ermöglicht eine Präzisierung von Ertragsanalysen. Dieser Bericht beschreibt den ersten Projektabschnitt, in welchem der Fokus vor allem auf der Initialisierung der messtechnischen Komponenten, sowie ersten Messungen und Analysen liegt.
A quantitative physical model for potential induced degradation of the shunting type (PID-s) in solar modules is introduced. Based on a drift and diffusion approach for sodium ions and atoms, it gives insight into the kinetics of degradation and the corresponding regeneration. A simple drift/source term is used to describe the time-dependent flux of Na-ions towards stacking faults at the surface of the solar cell. The assumed transport mechanism for Na+ ions through the SiN-layer uses the modified Stern-Eyring rate theory but our approach can also be adapted to other mechanisms. Several PID-s and regeneration curves of one-cell solar modules at T = 49°C and T = 90°C with 1000V potential difference between modul frame and cell were measured and least-square fits of the in-situ measured parallel resistance Rsh to the model were performed giving very good accordance.
Based on a few measurements, the model can predict PID-s and regeneration characteristics of solar modules under different conditions.