Auflistung nach Autor:in "Lowin, Maximilian"
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- TextdokumentFrom Physical to Virtual: Leveraging Drone Imagery to Automate Photovoltaic System Maintenance(INFORMATIK 2021, 2021) Lowin, Maximilian; Kellner, Domenic; Kohl, Tobias; Mihale-Wilson, CristinaOptimizing the maintenance of large-scale infrastructure can be a significant cost driver for small and medium-sized enterprises (SMEs). This paper presents a feasible approach to combine data from real-world physical structures collected through an automated maintenance process with cloud-based AI services to generate a meaningful virtual representation of such structures. We use photovoltaic systems as an exemplary physical structure and thermal imaging, collected through scheduled drone monitoring. With help of these unstructured data sources, we demonstrate our approach's applicability. Our solution artifact provides a lightweight AI application that is adoptable for other problem spaces, enabling an easier knowledge transfer from research to SMEs. By combining Cloud Computing with Machine Learning, the artifact identifies present and emerging damages of physical objects. It provides a virtual representation of the object's state and empowers a meaningful visualization.
- ZeitschriftenartikelSECAI – Sustainable Heating through Edge-Cloud-based AI Systems(HMD Praxis der Wirtschaftsinformatik: Vol. 60, No. 4, 2023) Kortum, Henrik; Hagen, Simon; Eleks, Marian; Rebstadt, Jonas; Remark, Florian; Lowin, Maximilian; Wilson, Cristina Mihale; Eberhardt, Birgid; Roß, Andree; Maihöfner, Dominik; Hinz, Oliver; Thomas, OliverEtwa 18 % der CO 2 -Emissionen in Deutschland entstehen durch die Beheizung, Kühlung und Warmwasserbereitstellung von Gebäuden, wobei mehr als 75 % der deutschen Haushalte fossile Brennstoffe wie Erdgas und Erdöl nutzen. Der in dieser Arbeit vorgestellte SECAI ( S ustainable heating through E dge- C loud-based A rtificial I ntelligence Systems)-Ansatz verfolgt das Ziel, die Heizungssteuerung in Mehrfamilienhäusern und damit den CO 2 -Verbrauch durch den Einsatz von Informationstechnologien zu reduzieren. Der SECAI-Ansatz betrachtet dabei das gesamte Ökosystem bestehend aus Sensoren, Einzelraumregelungen, Zentralheizung, Mietenden und Vermietenden. Dabei wird der Heizbedarf von Privatwohnungen KI-basiert analysiert, um darauf aufbauend optimierte und abgestimmte Heizpläne für Gebäudekomplexe und Wohnungen zu erstellen, die in der Lage sind, durch Edge-Cloud-Technologien, Sensorik und Federated Learning ad hoc und datenschutzkonform auf Änderungen im Nutzungsverhalten zu reagieren. Diese Informationen werden zudem für die KI-basierte Steuerung der zentralen Heizanlagen im Gebäude verwendet, in denen Wärme und Warmwasser für alle Wohnungen erzeugt wird. Hierfür betrachtet SECAI vier Ebenen. Diese reichen von Sensoren und Aktoren (Nano), über die Wohnung (Mikro) und das Gebäude (Meso) bis zu Gebäudekomplexen und gleicharten Gebäuden (Makro) und stehen bei der Beheizung in starker Abhängigkeit zueinander. Rund um die SECAI-Lösung entsteht dabei ein komplexes Ökosystem in dem Mietende, die Wohnungswirtschaft, Heizungshersteller und Anbieter von IoT-Lösungen mit Produkten und Diensten in Interaktion treten. Approximately 18% of CO2 emissions in Germany are caused by the heating, cooling and hot water supply of buildings, with more than 75% of households using fossil fuels such as natural gas and oil. The SECAI (Sustainable heating through Edge-Cloud-based Artificial Intelligence Systems) approach presented in this paper aims to reduce heating control in multi-residential buildings, and thus CO2 consumption, through the use of information technology. The SECAI approach considers the entire ecosystem consisting of sensors, individual room controls, central heating, tenants and landlords. This involves an AI-based analysis of the heating requirements of private apartments, based on which optimized and coordinated heating plans can be created for building complexes. Edge cloud technologies, sensor technology and federated learning enable these plans to react ad hoc and in compliance with data protection regulations to changes in usage behavior. The information is also used for AI-based control of the central heating systems within the building, where heating and hot water are generated for all apartments. For this purpose, SECAI considers four layers. These range from sensors and actuators (nano), to the apartment (micro), to the building (meso), to building complexes and same-type buildings (macro), and are highly interdependent. A complex ecosystem is being created around the SECAI solution in which tenants, the housing industry, heating manufacturers and providers of IoT solutions interact with products and services.
- TextdokumentTowards Designing a User-centric Decision Support System for Predictive Maintenance in SMEs(INFORMATIK 2021, 2021) Kellner, Domenic; Lowin, Maximilian; von Zahn, Moritz; Chen, JohannesIn manufacturing, small and medium-sized enterprises (SMEs) face global competition. In the field of predictive maintenance (PdM), artificial intelligence (AI) helps to prevent machine failures and has the potential to significantly reduce costs and increase process efficiency. Even though PdM has several benefits, it also entails considerable challenges for SMEs, especially when it comes to user interactions. In this short paper, we harness the design science methodology and discuss several problems regarding user interactions with predictive maintenance applications. We incorporate two different literature streams, namely, predictive maintenance and decision support systems. Finally, we present necessary design requirements, principles, features, and propose a research design to further develop and evaluate a user-centric PdM decision support system. Thereby, we contribute to making AI tangible in SMEs.
- TextdokumentTowards Predictive Maintenance as a Service in the Smart Housing Industry(INFORMATIK 2021, 2021) Lowin, Maximilian; Mihale-Wilson, CristinaMaintenance is a significant cost driver in many industries with tangible assets. Aiming to predict damages before they occur, this paper focuses on predictive maintenance (PdM) for smart buildings and apartments – a multi-billion-dollar market with substantial cost savings potential. Based on stakeholder groups’ heterogeneity within the smart housing industry, PdM cannot be a one-fits-all solution. To be effective, practitioners can enrich PdM with Artificial Intelligence (AI). However, to match very heterogeneous environments and the various needs of the stakeholders, PdM must be modular and flexible. Motivated by the challenges and peculiarities for implementing Predictive Maintenance as a Service (PdMaaS) in the smart housing industry, we provide a concept to support managers to overview and optimize complex PdM needs in complex and heterogeneous environments.