@inproceedings{WittigRuettersBragard2024,
  author    = {Wittig, M. and R{\"u}tters, Ren{\´e} and Bragard, Michael},
  title     = {Application of RL in control systems using the example of a rotatory inverted pendulum},
  series = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  booktitle = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-02-3},
  doi       = {10.33968/2024.53},
  pages     = {241 -- 248},
  year      = {2024},
  abstract  = {In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.},
  language  = {en}
}
@inproceedings{GrundAltherr2023,
  author    = {Grund, Raphael M. and Altherr, Lena},
  title     = {Development of an open source energy disaggregation tool for the home automation platform Home Assistant},
  series = {Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel},
  booktitle = {Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-01-6},
  doi       = {10.33968/2023.02},
  pages     = {11 -- 20},
  year      = {2023},
  abstract  = {In order to reduce energy consumption of homes, it is important to make transparent which devices consume how much energy. However, power consumption is often only monitored aggregated at the house energy meter. Disaggregating this power consumption into the contributions of individual devices can be achieved using Machine Learning. Our work aims at making state of the art disaggregation algorithms accessibe for users of the open source home automation platform Home Assistant.},
  language  = {en}
}
@inproceedings{AltherrDoeringFrauenrathetal.2024,
  author    = {Altherr, Lena and D{\"o}ring, Bernd and Frauenrath, Tobias and Groß, Rolf and Mohan, Nijanthan and Oyen, Marc and Schnittcher, Lukas and Voß, Norbert},
  title     = {DiggiTwin: ein interdisziplin{\"a}res Projekt zur Nutzung digitaler Zwillinge auf dem Weg zu einem klimaneutralen Geb{\"a}udebestand},
  series = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  booktitle = {Tagungsband AALE 2024 : Fit f{\"u}r die Zukunft: praktische L{\"o}sungen f{\"u}r die industrielle Automation},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-02-3},
  doi       = {10.33968/2024.67},
  pages     = {341 -- 346},
  year      = {2024},
  abstract  = {Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Geb{\"a}udeoptimierung. Grundlage f{\"u}r eine ganzheitliche Geb{\"a}ude{\"u}berwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplin{\"a}re Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgeb{\"a}ude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Geb{\"a}udezwilling entsteht. Dieser kann zur Optimierung des Geb{\"a}udebetriebs herangezogen werden, sowie als Basis f{\"u}r eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und k{\"u}nstlicher Intelligenz kann so ein pr{\"a}zises Monitoring wichtiger Geb{\"a}udedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Geb{\"a}udezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Geb{\"a}uden untersucht und konkrete L{\"o}sungen f{\"u}r die Geb{\"a}udeoptimierung entwickelt.},
  language  = {de}
}
@inproceedings{AltherrConzenElsenetal.2023,
  author    = {Altherr, Lena and Conzen, Max and Elsen, Ingo and Frauenrath, Tobias and Lyrmann, Andreas},
  title     = {Sensor retrofitting of existing buildings in an interdisciplinary teaching project at university level},
  series = {Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel},
  booktitle = {Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel},
  editor    = {Reiff-Stephan, J{\"o}rg and J{\"a}kel, Jens and Schwarz, Andr{\´e}},
  publisher = {le-tex publishing services GmbH},
  address   = {Leipzig},
  isbn      = {978-3-910103-01-6},
  doi       = {10.33968/2023.04},
  pages     = {31 -- 40},
  year      = {2023},
  abstract  = {Existing residential buildings have an average lifetime of 100 years. Many of these buildings will exist for at least another 50 years. To increase the efficiency of these buildings while keeping costs at reasonable rates, they can be retrofitted with sensors that deliver information to central control units for heating, ventilation and electricity. This retrofitting process should happen with minimal intervention into existing infrastructure and requires new approaches for sensor design and data transmission. At FH Aachen University of Applied Sciences, students of different disciplines work together to learn how to design, build, deploy and operate such sensors. The presented teaching project already created a low power design for a combined CO2, temperature and humidity measurement device that can be easily integrated into most home automation systems},
  language  = {en}
}