TY - CHAP A1 - Altherr, Lena A1 - Conzen, Max A1 - Elsen, Ingo A1 - Frauenrath, Tobias A1 - Lyrmann, Andreas ED - Reiff-Stephan, Jörg ED - Jäkel, Jens ED - Schwarz, André T1 - Sensor retrofitting of existing buildings in an interdisciplinary teaching project at university level T2 - Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel N2 - 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 KW - Building Automation KW - Smart Building KW - CO2 KW - Carbon Dioxide KW - Education Y1 - 2023 SN - 978-3-910103-01-6 U6 - http://dx.doi.org/10.33968/2023.04 N1 - 19. AALE-Konferenz. Luxemburg, 08.03.-10.03.2023. BTS Connected Buildings & Cities Luxemburg (Tagungsband unter https://doi.org/10.33968/2023.01) SP - 31 EP - 40 PB - le-tex publishing services GmbH CY - Leipzig ER - TY - CHAP A1 - Grund, Raphael M. A1 - Altherr, Lena ED - Reiff-Stephan, Jörg ED - Jäkel, Jens ED - Schwarz, André T1 - Development of an open source energy disaggregation tool for the home automation platform Home Assistant T2 - Tagungsband AALE 2023 : mit Automatisierung gegen den Klimawandel N2 - 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. KW - Home Automation Platform KW - Home Assistant KW - Open Source KW - Machine Learning KW - Energy Disaggregation Y1 - 2023 SN - 978-3-910103-01-6 U6 - http://dx.doi.org/10.33968/2023.02 N1 - 19. AALE-Konferenz. Luxemburg, 08.03.-10.03.2023. BTS Connected Buildings & Cities Luxemburg (Tagungsband unter https://doi.org/10.33968/2023.01) SP - 11 EP - 20 PB - le-tex publishing services GmbH CY - Leipzig ER - TY - CHAP A1 - Lahrs, Lennart A1 - Krisam, Pierre A1 - Herrmann, Ulf T1 - Envisioning a collaborative energy system planning platform for the energy transition at the district level T2 - The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems N2 - Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core. KW - Energy system planning KW - District energy planning platform KW - District data model KW - Renewable energy integration Y1 - 2023 U6 - http://dx.doi.org/10.52202/069564-0284 N1 - 25-30 JUNE, 2023, Las Palmas de Gran Canaria, Spain SP - 3163 EP - 3170 PB - Procedings of ECOS 2023 ER - TY - CHAP A1 - Neth, Jannik A1 - Schuba, Marko A1 - Brodkorb, Karsten A1 - Neugebauer, Georg A1 - Höner, Tim A1 - Hack, Sacha T1 - Digital forensics triage app for android T2 - ARES '23: Proceedings of the 18th International Conference on Availability, Reliability and Security N2 - Digital forensics of smartphones is of utmost importance in many criminal cases. As modern smartphones store chats, photos, videos etc. that can be relevant for investigations and as they can have storage capacities of hundreds of gigabytes, they are a primary target for forensic investigators. However, it is exactly this large amount of data that is causing problems: extracting and examining the data from multiple phones seized in the context of a case is taking more and more time. This bears the risk of wasting a lot of time with irrelevant phones while there is not enough time left to analyze a phone which is worth examination. Forensic triage can help in this case: Such a triage is a preselection step based on a subset of data and is performed before fully extracting all the data from the smartphone. Triage can accelerate subsequent investigations and is especially useful in cases where time is essential. The aim of this paper is to determine which and how much data from an Android smartphone can be made directly accessible to the forensic investigator – without tedious investigations. For this purpose, an app has been developed that can be used with extremely limited storage of data in the handset and which outputs the extracted data immediately to the forensic workstation in a human- and machine-readable format. KW - Android KW - Digital triage KW - Triage-app Y1 - 2023 SN - 9798400707728 U6 - http://dx.doi.org/10.1145/3600160.3605017 N1 - ARES 2023: The 18th International Conference on Availability, Reliability and Security. August 29 - September 1, 2023. Benevento, Italy. PB - ACM ER - TY - CHAP A1 - Schulte, Jonas A1 - Schwager, Christian A1 - Noureldin, Kareem A1 - May, Martin A1 - Teixeira Boura, Cristiano José A1 - Herrmann, Ulf T1 - Gradient controlled startup procedure of a molten-salt power-to-heat energy storage plant based on dynamic process simulation T2 - SolarPACES: Solar Power & Chemical Energy Systems N2 - The integration of high temperature thermal energy storages into existing conventional power plants can help to reduce the CO2 emissions of those plants and lead to lower capital expenditures for building energy storage systems, due to the use of synergy effects [1]. One possibility to implement that, is a molten salt storage system with a powerful power-to-heat unit. This paper presents two possible control concepts for the startup of the charging system of such a facility. The procedures are implemented in a detailed dynamic process model. The performance and safety regarding the film temperatures at heat transmitting surfaces are investigated in the process simulations. To improve the accuracy in predicting the film temperatures, CFD simulations of the electrical heater are carried out and the results are merged with the dynamic model. The results show that both investigated control concepts are safe regarding the temperature limits. The gradient controlled startup performed better than the temperature-controlled startup. Nevertheless, there are several uncertainties that need to be investigated further. KW - Power plants KW - Energy storage KW - Associated liquids Y1 - 2023 SN - 978-0-7354-4623-6 U6 - http://dx.doi.org/10.1063/5.0148741 SN - 1551-7616 (online) SN - 0094-243X (print) N1 - 27th International Conference on Concentrating Solar Power and Chemical Energy Systems 27 September–1 October 2021 Online IS - 2815 / 1 PB - AIP conference proceedings / American Institute of Physics CY - Melville, NY ER - TY - CHAP A1 - Schwager, Christian A1 - Angele, Florian A1 - Schwarzbözl, Peter A1 - Teixeira Boura, Cristiano José A1 - Herrmann, Ulf T1 - Model predictive assistance for operational decision making in molten salt receiver systems T2 - SolarPACES: Solar Power & Chemical Energy Systems N2 - Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy. KW - Power plants KW - Associated liquids KW - Decision theory KW - Electrochemistry Y1 - 2023 SN - 978-0-7354-4623-6 U6 - http://dx.doi.org/10.1063/5.0151514 SN - 1551-7616 (online) SN - 0094-243X (print) N1 - 27th International Conference on Concentrating Solar Power and Chemical Energy Systems 27 September–1 October 2021 Online IS - 2815 / 1 PB - AIP conference proceedings / American Institute of Physics CY - Melville, NY ER - TY - CHAP A1 - Thoma, Andreas A1 - Stiemer, Luc A1 - Braun, Carsten A1 - Fisher, Alex A1 - Gardi, Alessandro G. T1 - Potential of hybrid neural network local path planner for small UAV in urban environments T2 - AIAA SCITECH 2023 Forum N2 - This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance. Y1 - 2023 U6 - http://dx.doi.org/10.2514/6.2023-2359 N1 - AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, MD & Online PB - AIAA ER -