Editorial: Bildung gemeinsam verändern: Diskussionsbeiträge und Impulse aus Forschung und Praxis

Das vorliegende Themenheft 28 der Zeitschrift MedienPädagogik ist dem breit angelegten Motto «Bildung gemeinsam verändern» gewidmet. Dabei mag es einiger erläuternder Worte der Kontextualisierung bedürfen, um diese grosse Klammer verständlich zu machen. «Bildung gemeinsam verändern» lautete das Thema des vierten Jungen Forums für Medien und Hochschulentwicklung (JFMH), welches am 8. und 9. Juni 2015 an der Heinrich-Heine-Universität Düsseldorf stattfand (www.hhu.de/jfmh15). Mit diesem Heft liegen nun die Proceedings zur Tagung vor. Das JFMH ist eine Tagung(sreihe), deren Schwerpunkt auf Beiträgen von Young Researchers und Young Professionals im Feld von Medienpädagogik, Medien- und Hochschuldidaktik, E-Learning an Hochschulen, Schulen, (Aus-/Weiter-)Bildungsanbietern und allen weiteren Lernorten liegt. Das Forum wird seit 2012 unter der Schirmherrschaft der Deutschen Gesellschaft für Hochschuldidaktik (dghd), der Gesellschaft für Medien in der Wissenschaft (GMW), der Gesellschaft für Informatik (GI, Fachgruppe E-Learning) sowie der Deutschen Gesellschaft für Erziehungswissenschaft (DGfE, Junges Netzwerk Medienpädagogik der Sektion Medienpädagogik) ausgerichtet und rückt den Austausch zwischen den Fachbereichen wie auch zwischen forschungs- und anwendungsorientierten Perspektiven in den Vordergrund. Entsprechend den genannten Ansprüchen greift dieses Themenheft der Zeitschrift MedienPädagogik verschiedene Problemstellungen und Entwicklungsbedarfe auf, von denen wir drei konkreter benennen und ausführen möchten: a) Bildung interdisziplinär: der Gegenstand, der aus verschiedenen Perspektiven bearbeitet wird; b) Austausch zwischen Forschung und Praxis: die Vernetzung von Forschung und Praxis; c) Förderung von Young Researchers und Young Professionals: die Förderung von Praktikerinnen und Praktikern in frühen Karrierephasen

Analysis of building structure and topology based on Graph Theory

Individual views on a building product of people involved in the design process imply different models for planning and calculation. In order to interpret these geometrical, topological and semantical data of a building model we identify a structural component graph, a graph of room faces, a room graph and a relational object graph as aids and we explain algorithms to derive these relations. The application of the technique presented is demonstrated by the analysis and discretization of a sample model in the scope of building energy simulation

Investigating optimum cooling set point temperature and air velocity for thermal comfort and energy conservation in mixed-mode buildings in India

In warm and hot climates, ceiling fans and/or air conditioners (ACs) are used to maintain thermal comfort. Ceiling fans provide air movement near the skin, which enhances the evaporation of sweat, reduces heat stress, and enhances thermal comfort. This is also called the cooling effect. However, AC usage behaviour and the effects of elevated air speed through the use of ceiling fans on indoor operative temperature during AC usage are not widely studied. This study investigated the optimum AC (cooling) set point temperature and air velocity necessary for maintaining thermal comfort while achieving energy conservation, in mixed-mode buildings in India, through field studies by using used custom-built Internet of Things (IOT) devices. In the current study, the results indicate a 79% probability that comfort conditions can be maintained by achieving a temperature drop of 3K. If this drop can be achieved, as much as possible, through passive measures, the duration of AC operation and its energy consumption are reduced, at least by 67.5 and 58.4%, respectively. During the air-conditioned period, there is a possibility that the cooing effect is reduced because of increase in operative temperature due to ceiling fan operation. Therefore, the optimum solution is to maintain the highest AC set point and minimum fan speed setting that are acceptable

Structural Analysis based on the Product Model Standard IFC

In this paper we present a computer aided method supporting co-operation between different project partners, such as architects and engineers, on the basis of strictly three-dimensional models. The center of our software architecture is a product model, described by the Industry Foundation Classes (IFC) of the International Alliance for Interoperability (IAI). From this a geometrical model is extracted and automatically transferred to a computational model serving as a basis for various simulation tasks. In this paper the focus is set on the advantage of the fully three-dimensional structural analysis performed by p-version of the finite element analysis. Other simulation methods are discussed in a separate contribution of this Volume (Treeck 2004). The validity of this approach will be shown in a complex example

A gap-filling method for room temperature data based on autoencoder neural networks

This study explores the applicability of a deep learning-based approach for reconstructing missing room temperature data from different domains where relatively few training samples are available. For that purpose, the existing convolutional, long short-term memory (LSTM) and feed-forward autoencoders were combined with a suitable domain adaptation procedure. Eventually, the developed models were evaluated on data collected in four buildings with significant differences in thermal mass, design and location. The findings pointed out that the domain adaptation can be conducted effciently by using a small data sample from the target domain. Additionally, the results showed that the proposed model can reconstruct up to 80 % of the missing daily room temperature inputs with RMSE accuracy of 0.6 °C

Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating the field application of the approach. For that purpose, the aim of this paper is to propose the use of Physics-informed Denoising Autoencoders (PI-DAE) for missing data imputation in commercial buildings. In particular, the presented method enforces physics-inspired soft constraints to the loss function of a Denoising Autoencoder (DAE). In order to quantify the benefits of the physical component, an ablation study between different DAE configurations is conducted. First, three univariate DAEs are optimized separately on indoor air temperature, heating, and cooling data. Then, two multivariate DAEs are derived from the previous configurations. Eventually, a building thermal balance equation is coupled to the last multivariate configuration to obtain PI-DAE. Additionally, two commonly used benchmarks are employed to support the findings. It is shown how introducing physical knowledge in a multivariate Denoising Autoencoder can enhance the inherent model interpretability through the optimized physics-based coefficients. While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.Comment: Accepted for publication in Energy and Building

Interactive High-Performance Computing: Coupling a Thermoregulation Model to a CFD Code

ABSTRACT Nowadays, computers tend to get faster and more powerful, and are able to solve problems deemed unsolvable a decade ago. Especially in the field of civil engineering, problems involving computational fluid dynamics (CFD) are requiring a huge computational effort. Unfortunately, even the most powerful supercomputers are not able to solve a thermal CFD simulation of a complete building or even a room on a whole year basis, which would be necessary for thermal comfort predictions or an estimation of energy consumption. Thus, different approaches have to be used in order to obtain results on a whole year basis, such as zonal models, based on a coarse space and time discretisation. These results can then be used to act as boundary conditions for a highly detailed CFD analysis computed at certain characteristic snapshots in time, in order to get a better understanding of the internal airflow patterns in rooms, for example. Furthermore, the thermal behaviour of occupants can be modelled in detail by a human thermoregulation model, such as the model described by Fiala et al We will present coupling procedures from a human thermoregulation model to a CFD code. As code, an in-house CFD code specifically designed with parallel systems in mind, and described in [2] will be applied. Example computations will be presented, highlighting the coupling effects, as well as first validation results. Furthermore, an advanced, interactive data exploration system will be presented, allowing an interactive visualisation to explore simulation results during runtime, providing already preliminary views during computation time to scientists and engineers. REFERENCE

VOM LASERSCAN ZUM PLANUNGSTAUGLICHEN PRODUKTMODELL

Im Bereich der Altbausanierung und der Bestandserfassung im Bauwesen ist es häufig notwendig, bestehende Pläne hinsichtlich des Bauwerkszustandes zu aktualisieren oder, wenn diese Pläne nicht (mehr) zugänglich sind, gänzlich neue Planunterlagen des Ist-Zustandes zu erstellen. Ein komfortabler Weg, diese Bauwerksdaten zu erheben, eröffnet die Technologie der Laservermessung. Der vorliegende Artikel stellt in diesem Zusammenhang Ansätze zur Teilautomatisierung der Generierung eines dreidimensionalen Computermodells eines Bauwerkes vor. Als Ergebnis wird ein Volumenmodell bereitgestellt, in dem zunächst die geometrischen und topologischen Informationen über Flächen, Kanten und Punkte im Sinne eines B-rep Modells beschrieben sind. Die Objekte dieses Volumenmodells werden mit Verfahren aus dem Bereich der künstlichen Intelligenz analysiert und in Bauteilklassen systematisch kategorisiert. Die Kenntnis der Bauteilsemantik erlaubt es somit, aus den Daten ein Bauwerks-Produktmodell abzuleiten und dieses einzelnen Fachplanern – etwa zur Erstellung eines Energiepasses – zugänglich zu machen. Der Aufsatz zeigt den erfolgreichen Einsatz virtueller neuronaler Netze im Bereich der Bestandserfassung anhand eines komplexen Beispiels