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Flexibility estimation is the first step necessary to incorporate building energy systems into demand side management programs. We extend a known method for temporal flexibility estimation from literature to a real-world residential heat pump system, solely based on historical cloud data. The method proposed relies on robust simplifications and estimates employing process knowledge, energy balances and manufacturer's information. Resulting forced and delayed temporal flexibility, covering both domestic hot water and space heating demands as constraints, allows to derive a flexibility range for the heat pump system. The resulting temporal flexibility lay within the range of 24 minutes and 6 hours for forced and delayed flexibility, respectively. This range provides new insights into the system's behaviour and is the basis for estimating power and energy flexibility - the first step necessary to incorporate building energy systems into demand side management programs.
With the digitalisation, and the increased connectivity between manufacturing systems emerging in this context, manufacturing is shifting towards decentralised, distributed concepts. Still, for manufacturing scenarios manual input or augmentation of data is required at system boundaries. Especially in distributed manufacturing environments, like Cloud Manufacturing (CMfg) systems, constant changes to the available manufacturing resources and products pose challenges for establishing connections between them. We propose a feature-oriented representation of concepts, especially from the manufacturing domain, which serves as the basis for (semi-) automatically linking, e.g., manufacturing resources and products. This linking methodologies, as well as knowledge inferred using it, is then used to support distributed manufacturing, especially in CMfg environments, and enhance product development. The concepts and methodologies are to be evaluated in a real world learning factory.
A trend from centralized to decentralized production is emerging in the manufacturing domain leading to new and innovative approaches for long-established production methods. A technology supporting this trend is Cloud Manufacturing, which adapts technologies and concepts known from cloud computing to the manufacturing domain. A core aspect of Cloud Manufacturing is representing knowledge about manufacturing, e.g., machine capabilities, in a suitable form. This knowledge representation should be flexible and adaptable so that it fits across various manufacturing domains, but, at the same time, should also be specific and exhaustive. We identify three core capabilities that such a platform has to support, i.e., the product, the process and the production.We propose representing this knowledge in semantically specified knowledge graphs, essentially creating three through features interconnected ontologies each representing a facet of manufacturing. Finally, we present an exemplary implementation of a Cloud Manufacturing platform using this representation and its advantages.
Bubble column humidifiers (BCHs) are frequently used for the humidification of air in various water treatment applications. A potential but not yet profoundly investigated application of such devices is the treatment of oily wastewater. To evaluate this application, the accumulation of an oil-water emulsion using a BCH is experimentally analyzed. The amount of evaporating water vapor can be evaluated by measuring the humidity ratio of the outlet air. However, humidity measurements are difficult in close to saturated conditions, as the formation of liquid droplets on the sensor impacts the measurement accuracy. We use a heating section after the humidifier, such that no liquid droplets are formed on the sensor. This enables us a more accurate humidity measurement. Two batch measurement runs are conducted with (1) tap water and (2) an oil-water emulsion as the respective liquid phase. The humidity measurement in high humidity conditions is highly accurate with an error margin of below 3 % and can be used to predict the oil concentration of the remaining liquid during operation. The measured humidity ratio corresponds with the removed amount of water vapor for both tap water and the accumulation of an oil-water emulsion. Our measurements show that the residual water content
in the oil-water emulsion is below 4 %.
Stress testing is part of today’s bank risk management and often required by the governing regulatory authority. Performing such a stress test with stress scenarios derived from a distribution, instead of pre-defined expert scenarios, results in a systematic approach in which new severe scenarios can be discovered. The required scenario distribution is obtained from historical time series via a Vector-Autoregressive time series model. The worst-case search, i.e. finding the scenario yielding the most severe situation for the bank, can be stated as an optimization problem. The problem itself is a constrained optimization problem in a high-dimensional search space. The constraints are the box constraints on the scenario variables and the plausibility of a scenario.
The latter is expressed by an elliptic constraint. As the evaluation of the stress scenarios is performed with a simulation tool, the optimization problem can be seen as black-box optimization problem. Evolution Strategy, a well-known optimizer for black-box problems, is applied here. The necessary adaptations to the algorithm are explained and a set of different algorithm design choices are investigated. It is shown that a simple box constraint handling method, i.e. setting variables which violate a box constraint to the respective boundary of the feasible domain, in combination with a repair of implausible scenarios provides good results.
Business Analytics zählt zu den Zukunftsthemen im Controlling. In der Controllinglehre spielt Analytics bisher aber nur eine untergeordnete Rolle. Der Beitrag beschreibt ein innovatives Lehrprojekt, das Studierende im Masterstudium Accounting, Controlling & Finance an der FH Vorarlberg befähigt, controllingrelevante Fragestellungen im Kontext von Business Analytics eigenständig zu beantworten. Gleichzeitig erlernen die Studierenden den Umgang mit der Open-Source-Software R.
The increasing digitalisation of daily routines confronts people with frequent privacy decisions. However, obscure data processing often leads to tedious decision-making and results in unreflective choices that unduly compromise privacy. Serious Games could be applied to encourage teenagers and young adults to make more thoughtful privacy decisions. Creating a Serious Game (SG) that promotes privacy awareness while maintaining an engaging gameplay requires, however, a carefully balanced game concept. This study explores the benefits of an online role-playing boardgame as a co-designing activity for creating SGs about privacy. In a between-subjects trial, student groups and educator/researcher groups were taking the roles of player, teacher, researcher and designer to co-design a balanced privacy SG concept. Using predefined design proposal cards or creating their own, students and educators played the online boardgame during a video conference session to generate game ideas, resolve potential conflicts and balance the different SG aspects. The comparative results of the present study indicate that students and educators alike perceive support from role-playing when ideating and balancing SG concepts and are happy with their playfully co-designed game concepts. Implications for supporting SG design with role-playing in remote collaboration scenarios are conclusively synthesised.
One goal of the project described in this paper is to create learning algorithms for machines and robots that lack a precise virtual controller for correct simulations. Using a digital twin approach, the developed mixed reality application aims for an overlay of a virtual robot model with the real world counterpart using Microsoft HoloLens 2 smart glasses. The application should help users to have an inside look into the results of the learning algorithm and therefore supervise and improve those results. The main focus of this paper is the visual representation of the digital twin on the smart glasses. One of the challenges is the level of abstraction and specific use of shaders (program code defining material attributes) to help the user differentiating between virtual and real objects. Therefore different presentation methods are described and evaluated. Study results with 48 persons show that the most abstract representation (wireframe) scores lowest, whereas a half-transparent model works best.