82
4 State of the Art
tools (e.g., MATLAB, EnergyPlus, and Dymola), to ensure that the special requirements of every sub model are met by choosing the optimum software. To
couple the different simulation models, the extensible open-source software platform ‘Building Controls Virtual Test Bed’ (BCVTB) is used [He+2013, p. 306].
It allows the runtime coupling of different simulation software, supports data
exchange and hierarchical combination of different modeling semantics. Proving
the advantages of their co-simulation approach, the authors create different production scenarios varying the climatic and production conditions. The approach
allows the user to make predictions regarding energy optimization measures in
production systems.
Eberspaecher et al. present an approach combining power measurement
data and control signal information with consumption data that has been generated using static and dynamic simulation models [Eb+2014]. In a second step
a situation-based optimization is carried out to reduce energy consumption of
machine tools. Eberspaecher et al. develop a general machine tool component library to be able to calculate with detailed electric load curves instead of
using operating states. They combine their simulation approach to estimate the
energy demand on machine and component level with a monitoring approach
to allow real time energy demand monitoring. In combination with a component and an operating state optimizer the optimal parameter configuration for an
energy-optimal production is found.
In 2017, Baumann et al. extend their approach presented in section 4.1.2 by
a simulation model to identify energetic losses occurring in manufacturing processes [Ba+2017]. They generate necessary process parameters for the simulation
model for each product type by a complex data analysis of production and energy
data in MATLAB and then provide them in tables and related files for the simulation model which is modeled using Modelica as a language and SimulationX as
a software tool. After a validation process, the model data is exported as a functional mockup unit (FMU) and then integrated in the buildup Advanced Planning
and Scheduling (APS) tool chain containing the optimization module. The consideration of the energy demand of the depicted manufacturing processes thus
enables the planner to reduce the energy demand and the associated costs of the
production process [Ba+2017, p. 67].
Sobottka, Kamhuber, and Sihn present an approach aiming at the development of a new planning tool to increase the energy efficiency of productions using
a hybrid simulation and a multi-criteria optimization [SKS2017]. In several steps,
they develop an optimization approach based on genetic algorithms to expand the
target system of planning to include energy efficiency targets in addition to the
classic economic targets. This extension requires a simultaneous mapping of the
4 State of the Art
tools (e.g., MATLAB, EnergyPlus, and Dymola), to ensure that the special requirements of every sub model are met by choosing the optimum software. To
couple the different simulation models, the extensible open-source software platform ‘Building Controls Virtual Test Bed’ (BCVTB) is used [He+2013, p. 306].
It allows the runtime coupling of different simulation software, supports data
exchange and hierarchical combination of different modeling semantics. Proving
the advantages of their co-simulation approach, the authors create different production scenarios varying the climatic and production conditions. The approach
allows the user to make predictions regarding energy optimization measures in
production systems.
Eberspaecher et al. present an approach combining power measurement
data and control signal information with consumption data that has been generated using static and dynamic simulation models [Eb+2014]. In a second step
a situation-based optimization is carried out to reduce energy consumption of
machine tools. Eberspaecher et al. develop a general machine tool component library to be able to calculate with detailed electric load curves instead of
using operating states. They combine their simulation approach to estimate the
energy demand on machine and component level with a monitoring approach
to allow real time energy demand monitoring. In combination with a component and an operating state optimizer the optimal parameter configuration for an
energy-optimal production is found.
In 2017, Baumann et al. extend their approach presented in section 4.1.2 by
a simulation model to identify energetic losses occurring in manufacturing processes [Ba+2017]. They generate necessary process parameters for the simulation
model for each product type by a complex data analysis of production and energy
data in MATLAB and then provide them in tables and related files for the simulation model which is modeled using Modelica as a language and SimulationX as
a software tool. After a validation process, the model data is exported as a functional mockup unit (FMU) and then integrated in the buildup Advanced Planning
and Scheduling (APS) tool chain containing the optimization module. The consideration of the energy demand of the depicted manufacturing processes thus
enables the planner to reduce the energy demand and the associated costs of the
production process [Ba+2017, p. 67].
Sobottka, Kamhuber, and Sihn present an approach aiming at the development of a new planning tool to increase the energy efficiency of productions using
a hybrid simulation and a multi-criteria optimization [SKS2017]. In several steps,
they develop an optimization approach based on genetic algorithms to expand the
target system of planning to include energy efficiency targets in addition to the
classic economic targets. This extension requires a simultaneous mapping of the