@article{LeiseEsserEichenlaubetal.2021,
  author    = {Leise, Philipp and Eßer, Arved and Eichenlaub, Tobias and Schleiffer, Jean-Eric and Altherr, Lena and Rinderknecht, Stephan and Pelz, Peter F.},
  title     = {Sustainable system design of electric powertrains - comparison of optimization methods},
  series = {Engineering Optimization},
  journal   = {Engineering Optimization},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {0305-215X},
  doi       = {10.1080/0305215X.2021.1928660},
  year      = {2021},
  abstract  = {The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal 'climate action' stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.},
  language  = {en}
}
@incollection{AltherrLeise2021,
  author    = {Altherr, Lena and Leise, Philipp},
  title     = {Resilience as a concept for mastering uncertainty},
  series = {Mastering Uncertainty in Mechanical Engineering},
  booktitle = {Mastering Uncertainty in Mechanical Engineering},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-78353-2},
  doi       = {10.1007/978-3-030-78354-9},
  pages     = {412 -- 417},
  year      = {2021},
  language  = {en}
}
@incollection{AltherrLeisePfetschetal.2021,
  author    = {Altherr, Lena and Leise, Philipp and Pfetsch, Marc E. and Schmitt, Andreas},
  title     = {Optimal design of resilient technical systems on the example of water supply systems},
  series = {Mastering Uncertainty in Mechanical Engineering},
  booktitle = {Mastering Uncertainty in Mechanical Engineering},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-78356-3},
  pages     = {429 -- 433},
  year      = {2021},
  language  = {en}
}
@incollection{LeiseAltherr2021,
  author    = {Leise, Philipp and Altherr, Lena},
  title     = {Experimental evaluation of resilience metrics in a fluid system},
  series = {Mastering Uncertainty in Mechanical Engineering},
  booktitle = {Mastering Uncertainty in Mechanical Engineering},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-78356-3},
  pages     = {442 -- 447},
  year      = {2021},
  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{TischbeinKeanVertgewalletal.2023,
  author    = {Tischbein, Franziska and Kean, Kilian and Vertgewall, Chris Martin and Ulbig, Andreas and Altherr, Lena},
  title     = {Determination of the topology of low-voltage distribution grids using cluster methods},
  series = {27th International Conference on Electricity Distribution (CIRED 2023)},
  booktitle = {27th International Conference on Electricity Distribution (CIRED 2023)},
  publisher = {IEEE},
  isbn      = {978-1-83953-855-1},
  doi       = {10.1049/icp.2023.0478},
  pages     = {1 -- 5},
  year      = {2023},
  abstract  = {Due to the decarbonization of the energy sector, the electric distribution grids are undergoing a major transformation, which is expected to increase the load on the operating resources due to new electrical loads and distributed energy resources. Therefore, grid operators need to gradually move to active grid management in order to ensure safe and reliable grid operation. However, this requires knowledge of key grid variables, such as node voltages, which is why the mass integration of measurement technology (smart meters) is necessary. Another problem is the fact that a large part of the topology of the distribution grids is not sufficiently digitized and models are partly faulty, which means that active grid operation management today has to be carried out largely blindly. It is therefore part of current research to develop methods for determining unknown grid topologies based on measurement data. In this paper, different clustering algorithms are presented and their performance of topology detection of low voltage grids is compared. Furthermore, the influence of measurement uncertainties is investigated in the form of a sensitivity analysis.},
  language  = {en}
}
@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}
}
@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}
}