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Auflistung nach Schlagwort "Energy"

1 - 3 von 3
  • Konferenzbeitrag
    Application and Testing of Business Processes in the Energy Domain
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, 2017) Böhmer, Kristof; Stertz, Florian; Hildebrandt, Tobias; Rinderle-Ma, Stefanie; Eibl, Günther; Ferner, Cornelia; Burkhart, Sebastian; Engel, Dominik
    The energy domain currently struggles with radical legal and technological changes, such as, smart meters. This results in new use cases which can be implemented based on business process technology. Understanding and automating business processes requires to model and test them. However, existing process testing approaches frequently struggle with the testing of process resources, such as ERP systems, and negative testing. Hence, this work presents a toolchain which tackles that limitations. The approach uses an open source process engine to generate event logs and applies process mining techniques in a novel way.
  • Textdokument
    NeMeSys – Energy Adaptive Graph Pattern Matching on NUMA-based Multiprocessor Systems
    (BTW 2019, 2019) Krause, Alexander; Ungethüm, Annett; Kissinger, Thomas; Habich, Dirk; Lehner, Wolfgang
    NeMeSys is a NUMA-aware graph pattern processing engine, which leverages intelligent resource management for energy adaptive processing. With modern server systems incorporating an increasing amount of main memory, we can store graphs and compute analytical graph algorithms like graph pattern matching completely in-memory. Such server systems usually contain several powerful multiprocessors, which come with a high demand for energy. We demonstrate, that graph patterns can be processed in given performance constraints while saving energy, which would be wasted without proper controlling.
  • Textdokument
    Real-world application benchmark for QAOA algorithm for an electromobility use case
    (INFORMATIK 2022, 2022) Federer,Marika; Müssig,Daniel; Lenk,Steve; Lässig,Jörg
    To reduce $CO_2$ emissions in the mobility sector, battery electric service vehicles might play an important role in the future. Here, an optimal charging scheduling use case will be presented which includes local solar power generation for minimizing the power grid usage for electric service vehicles. Different formulations of the use case are given to illustrate the differences for classical and quantum-based optimization using a mixed integer linear program and a quadratic unconstrained binary optimization program, respectively. Addtionally, we study the complexity of our benchmark experiments by characterizing the respective QUBO matrices and the optimization landscapes. It is shown how the setting of the parameters of a certain experiment and its penalty function influences the complexity for a quantum-based optimizer. Additionally, we present a comparison of the computing times and summarize the current state of gate-based quantum computing for electromobility.