TY - RPRT A1 - Bauer, Lucia A1 - Benecken, Gerd A1 - Bücker, Dominikus A1 - Buff, Alexander A1 - Carlton, Katrina A1 - Feldmeier, Franz A1 - Flatscher, Simon A1 - Hack, Andreas A1 - Halt, Manfred A1 - Jell, Peter A1 - Köster, Heinrich A1 - Kucich, Martin A1 - Manzinger, Franziska A1 - Mecking, Simon A1 - Paus, Inger A1 - Rex, Steffen A1 - Schanda, Ulrich A1 - Schreyer, Manuela A1 - Spindler, Uli A1 - Stadler, Constanze A1 - Stahnke, Svenja A1 - Stiegler, Gertrud A1 - Walser, Georg A1 - Wambsganß, Mathias A1 - Wellisch, Ulrich T1 - Forschungsbericht 2014 N2 - Mit dem jährlich erscheinenden Forschungsbericht möchte die Hochschule Rosenheim einen Einblick in ihre vielfältigen Projekte und Aktivitäten der angewandten Forschung und Entwicklung geben. Im Forschungsbericht 2014 wird über Vorhaben im Jahr 2014 berichtet. T3 - Schriftenreihen - Forschungsbericht - 3 KW - Forschung Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-12317 ER - TY - RPRT A1 - Ackermann, Timo A1 - Angermeier, Martin A1 - Auer, Veronika A1 - Beneken, Gerd A1 - Bernhardt, Andreas A1 - Botsch, Rafael A1 - Bücker, Dominikus A1 - Hager, Ralf A1 - Hauck-Bauer, Eva A1 - Heigl, Martin A1 - Hirschmüller, Sebastian A1 - Höllmüller, Janett A1 - Karlinger, Peter A1 - Köster, Heinrich A1 - Krause, Harald A1 - Kucich, Martin A1 - Matthias, Kira A1 - Patzl, Victoria A1 - Plönnigs, René A1 - Posch, Georg A1 - Schanda, Ulrich A1 - Scheerer, Josua A1 - Schlecht, Johannes A1 - Schmidt, Jochen A1 - Stichler, Markus A1 - Uhl, Cornelius A1 - Viehhauser, Peter A1 - Weber, Gabriel A1 - Wolf, Christopher A1 - Zagler, Stefan A1 - Zentgraf, Peter T1 - Forschungsbericht 2013 N2 - Mit dem jährlich erscheinenden Forschungsbericht möchte die Hochschule Rosenheim einen Einblick in ihre vielfältigen Projekte und Aktivitäten der angewandten Forschung und Entwicklung geben. Im Forschungsbericht 2013 wird über Vorhaben im Jahr 2013 berichtet. T3 - Schriftenreihen - Forschungsbericht - 2 KW - Forschung Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-12324 ER - TY - JOUR A1 - Neumayer, Martin A1 - Stecher, Dominik A1 - Grimm, Sebastian A1 - Maier, Andreas A1 - Bücker, Dominikus A1 - Schmidt, Jochen T1 - Fault and anomaly detection in district heating substations: A survey on methodology and data sets JF - Energy N2 - District heating systems are essential building blocks for affordable, low-carbon heat supply. Early detection and elimination of faults is crucial for the efficiency of these systems and necessary to achieve the low temperatures targeted for 4th generation district heating systems. Especially methods for fault and anomaly detection in district heating substations are currently of high interest, as faults in substations can be repaired quickly and inexpensively, and smart meter data are becoming widely available. In this paper, we review recent scientific publications presenting data-driven approaches for fault and anomaly detection in district heating substations with a focus on methods and data sets. Our review indicates that researchers use a wide variety of methods, mostly focusing on unsupervised anomaly detection rather than fault detection. This is due to a lack of labeled data sets, preventing the use of supervised learning methods and quantitative analysis. Together with the lack of publicly available data sets, this impedes the accurate comparison of individual methods. To overcome this impediment, increase the comparability of different methods and foster competition, future research should focus on establishing publicly available data sets, and industry-relevant metrics as benchmarks. KW - District heating systems KW - Fault/anomaly detection KW - Machine learning Y1 - 2023 U6 - https://doi.org/10.1016/j.energy.2023.127569 VL - 276 SP - 127569 ER - TY - CHAP A1 - Alasmar, Odai A1 - Neumayer, Martin A1 - Bucker, Dominikus T1 - Data Augmentation Technique for Dealing with Multi-Resolution Issues in Segmentation of Photovoltaic Systems in Aerial Imagery T2 - 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET N2 - Given the urgent global challenges posed by climate change the transition to renewable energy and the reduction of carbon emissions is of paramount importance. Automatically detecting photovoltaic (PV) systems in aerial imagery is crucial for understanding, planning and optimizing our energy infras- tructure. However, the task is complicated by the significant variability in the ground sampling distance (GSD) of available aerial imagery. This directly affects the spatial resolution and consequently the quality and applicability of training data for deep learning models. Typically, available datasets have high- resolution imagery, making them preferable for annotation due to their detailed visual information. In contrast, real-world applications often have to deal with lower-resolution images. This discrepancy poses a challenge for training models that can make accurate predictions under varying real-world conditions. Our research presents an approach that deliberately degrades the resolution of high-quality training images to match the lower- resolution images encountered in real-world applications. By training on degraded resolution data, we ensure that the models are not overly tuned to high-resolution features that are often not present in the target application scenarios. The comparative analysis of three state-of-the-art models, with and without ap- plying our resolution degradation method, shows a considerable improvement in prediction accuracy and model generalisability when applying our resolution degradation method. This study highlights the importance of matching the resolution of training data to that of real-world applications to develop robust and universally applicable PV detection models. Y1 - 2024 U6 - https://doi.org/10.1109/ICECET61485.2024.10698167 SP - 1 EP - 6 PB - IEEE ER - TY - JOUR A1 - Stecher, Dominik A1 - Neumayer, Martin A1 - Ramachandran, Adithya A1 - Hort, Anastasia A1 - Maier, Andreas A1 - Bücker, Dominikus A1 - Schmidt, Jochen T1 - Creating a labelled district heating data set: From anomaly detection towards fault detection JF - Energy N2 - For an efficient operation of district heating systems, being able to detect anomalies and faults at an early stage is highly desirable. Here, data-driven machine learning methods can be a cornerstone, particularly for fault detection in district heating substations, where the availability of heat meter data keeps increasing. However, the creation of data sets suitable for training such machine learning models poses challenges to researchers and practitioners alike. To address this problem, we propose a systematic and domain-specific process for data set creation for fault detection in the form of practical guidelines. This process concretizes the data science and data mining cross-industry standard CRISP-DM for the district heating domain and focuses on the process steps of goal definition, data acquisition and understanding, and data curation. We aim to enable researchers and practitioners to create data sets for fault detection in the district heating domain and therefore also enable the creation or improvement of machine learning models in this domain. In addition, we propose a minimum viable feature set for fault detection in district heating networks with the goal of enabling better cooperation between researchers and easier transfer of the resulting machine learning models, to better proliferate new progress in the field. KW - Machine Learning Y1 - 2024 U6 - https://doi.org/10.1016/j.energy.2024.134016 VL - 313 SP - 134016 PB - Elsevier ER -