loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Jens Lippel ; Martin Becker and Thomas Zielke

Affiliation: University of Applied Sciences Düsseldorf, Münsterstr. 156, 40476, Düsseldorf and Germany

Keyword(s): Deep Neural Networks, Machine Learning, Dynamic Processes, Gas-fired Absorption Heat Pump, Simulation, Modeling.

Related Ontology Subjects/Areas/Topics: Application Domains ; Case Studies ; Formal Methods ; Health Engineering and Technology Applications ; Hydraulic and Pneumatic Systems ; Neural Nets and Fuzzy Systems ; Neural Rehabilitation ; Neurotechnology, Electronics and Informatics ; Non-Linear Systems ; Simulation and Modeling ; Simulation Tools and Platforms

Abstract: Deriving mathematical models for the simulation of dynamic processes is costly and time-consuming. This paper examines the possibilities of deep neural networks (DNNs) as a means to facilitate and accelerate this step in development. DNNs are machine learning models that have become a state-of-the-art solution to a wide range of data analysis and pattern recognition tasks. Unlike mathematical modeling approaches, DNN approaches require little to no domain-specific knowledge. Given a sufficient amount of data, a model of the complex nonlinear input-to-output relations of a dynamic system can be learned autonomously. To validate this DNN based modeling approach, we use the example of a gas-fired absorption heat pump. The DNN is learned based on several measurement series recorded during a hardware-in-the-loop (HiL) simulation of the heat pump. A mathematical reference model of the heat pump that was tested in the same HiL environment is used for a comparison of a mathematical and a DNN based modeling approach. Our results show that DNNs can yield models that are comparable to the reference model. The presented methodology covers the data preprocessing, the learning of the models and their validation. It can be easily transferred to more complex dynamic processes. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.92.96.247

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Lippel, J.; Becker, M. and Zielke, T. (2019). Modeling Dynamic Processes with Deep Neural Networks: A Case Study with a Gas-fired Absorption Heat Pump. In Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-381-0; ISSN 2184-2841, SciTePress, pages 317-326. DOI: 10.5220/0007932903170326

@conference{simultech19,
author={Jens Lippel. and Martin Becker. and Thomas Zielke.},
title={Modeling Dynamic Processes with Deep Neural Networks: A Case Study with a Gas-fired Absorption Heat Pump},
booktitle={Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2019},
pages={317-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007932903170326},
isbn={978-989-758-381-0},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Modeling Dynamic Processes with Deep Neural Networks: A Case Study with a Gas-fired Absorption Heat Pump
SN - 978-989-758-381-0
IS - 2184-2841
AU - Lippel, J.
AU - Becker, M.
AU - Zielke, T.
PY - 2019
SP - 317
EP - 326
DO - 10.5220/0007932903170326
PB - SciTePress