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 - Kellner, Robert T1 - Making Effective Videos and Live Online Lectures Quickly with a Live Composite Format JF - International Journal of Innovation in Science and Mathematics Education (IJISME) N2 - Instructional videos are the dominant mode of content delivery in higher education. They can be an effective tool for delivering educational content, providing flexibility in time and location and improving students' understanding of the material. The effectiveness of such videos may be enhanced by applying principles for multimedia design, which are based on a cognitive theory of multimedia learning. One way to adhere to those principles is by showing the instructor together with additional visuals on screen. Some studies suggest that this may have a positive effect on student learning and overall performance. The combination of the instructor and other visuals is usually done in post-production editing, which can be a time-consuming process. In this paper, a live composition video format is proposed, where the instructor is integrated into the presentation during recording. Using this approach, it is possible for the instructor to interact with added visuals directly, requiring little to no post-production. Furthermore, this method can also be used to enrich and increase the efficacy of synchronous live online lectures. KW - Video KW - Multimedia Learning KW - Online Lectures KW - Live Composite KW - Video-based Learning KW - Online-Lehre KW - Instructor Presence Y1 - 2025 U6 - https://doi.org/10.30722/IJISME.31.05.003 VL - 2023 IS - Vol. 31 No. 5 SP - 29 EP - 39 PB - University of Sydney ER -