TY - CHAP A1 - Blanke, Tobias A1 - Schmidt, Katharina S. A1 - Göttsche, Joachim A1 - Döring, Bernd A1 - Frisch, Jérôme A1 - van Treeck, Christoph ED - Weidlich, Anke ED - Neumann, Dirk ED - Gust, Gunther ED - Staudt, Philipp ED - Schäfer, Mirko T1 - Time series aggregation for energy system design: review and extension of modelling seasonal storages T2 - Energy Informatics N2 - Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results. KW - Energy system KW - Renewable energy KW - Mixed integer linear programming (MILP) KW - Typical periods KW - Time-series aggregation Y1 - 2022 U6 - http://dx.doi.org/10.1186/s42162-022-00208-5 SN - 2520-8942 N1 - Proceedings of the 11th DACH+ Conference on Energy Informatics, 15-16 September 2022, Freiburg, Germany. VL - 5 IS - 1, Article number: 17 SP - 1 EP - 14 PB - Springer Nature ER - TY - CHAP A1 - Ulmer, Jessica A1 - Braun, Sebastian A1 - Cheng, Chi-Tsun A1 - Dowey, Steve A1 - Wollert, Jörg T1 - Usage of digital twins for gamification applications in manufacturing T2 - Procedia CIRP N2 - Gamification applications are on the rise in the manufacturing sector to customize working scenarios, offer user-specific feedback, and provide personalized learning offerings. Commonly, different sensors are integrated into work environments to track workers’ actions. Game elements are selected according to the work task and users’ preferences. However, implementing gamified workplaces remains challenging as different data sources must be established, evaluated, and connected. Developers often require information from several areas of the companies to offer meaningful gamification strategies for their employees. Moreover, work environments and the associated support systems are usually not flexible enough to adapt to personal needs. Digital twins are one primary possibility to create a uniform data approach that can provide semantic information to gamification applications. Frequently, several digital twins have to interact with each other to provide information about the workplace, the manufacturing process, and the knowledge of the employees. This research aims to create an overview of existing digital twin approaches for digital support systems and presents a concept to use digital twins for gamified support and training systems. The concept is based upon the Reference Architecture Industry 4.0 (RAMI 4.0) and includes information about the whole life cycle of the assets. It is applied to an existing gamified training system and evaluated in the Industry 4.0 model factory by an example of a handle mounting. KW - Gamification KW - Digital Twin KW - Support System Y1 - 2022 U6 - http://dx.doi.org/10.1016/j.procir.2022.05.044 SN - 2212-8271 N1 - 55th CIRP Conference on Manufacturing Systems VL - 107 SP - 675 EP - 680 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Amir, Malik A1 - Bauckhage, Christian A1 - Chircu, Alina A1 - Czarnecki, Christian A1 - Knopf, Christian A1 - Piatkowski, Nico A1 - Sultanow, Eldar T1 - What can we expect from quantum (digital) twins? N2 - Digital twins enable the modeling and simulation of real-world entities (objects, processes or systems), resulting in improvements in the associated value chains. The emerging field of quantum computing holds tremendous promise for evolving this virtualization towards Quantum (Digital) Twins (QDT) and ultimately Quantum Twins (QT). The quantum (digital) twin concept is not a contradiction in terms - but instead describes a hybrid approach that can be implemented using the technologies available today by combining classical computing and digital twin concepts with quantum processing. This paper presents the status quo of research and practice on quantum (digital) twins. It also discuses their potential to create competitive advantage through real-time simulation of highly complex, interconnected entities that helps companies better address changes in their environment and differentiate their products and services. KW - Artificial Intelligence KW - Digital Twin Evolution KW - Machine Learning KW - Quantum Computing KW - Quantum Machine Learning Y1 - 2022 N1 - 17. Internationale Tagung Wirtschaftsinformatik, 21. – 23. Februar 2022, Nürnberg (online) SP - 1 EP - 14 PB - AIS Electronic Library (AISeL) ER - TY - CHAP A1 - Wiegner, Jonas A1 - Volker, Hanno A1 - Mainz, Fabian A1 - Backes, Andreas A1 - Löken, Michael A1 - Hüning, Felix T1 - Wiegand-effect-powered wireless IoT sensor node T2 - Sensoren und Messsysteme 2022 N2 - In this article we describe an Internet-of-Things sensing device with a wireless interface which is powered by the oftenoverlooked harvesting method of the Wiegand effect. The sensor can determine position, temperature or other resistively measurable quantities and can transmit the data via an ultra-low power ultra-wideband (UWB) data transmitter. With this approach we can energy-self-sufficiently acquire, process, and wirelessly transmit data in a pulsed operation. A proof-of-concept system was built up to prove the feasibility of the approach. The energy consumption of the system is analyzed and traced back in detail to the individual components, compared to the generated energy and processed to identify further optimization options. Based on the proof-of-concept, an application demonstrator was developed. Finally, we point out possible use cases. Y1 - 2022 SN - 978-3-8007-5835-7 SP - 255 EP - 260 PB - VDE Verlag GmbH CY - Berlin ER -