@techreport{BauerBeneckenBueckeretal.2015, author = {Bauer, Lucia and Benecken, Gerd and B{\"u}cker, Dominikus and Buff, Alexander and Carlton, Katrina and Feldmeier, Franz and Flatscher, Simon and Hack, Andreas and Halt, Manfred and Jell, Peter and K{\"o}ster, Heinrich and Kucich, Martin and Manzinger, Franziska and Mecking, Simon and Paus, Inger and Rex, Steffen and Schanda, Ulrich and Schreyer, Manuela and Spindler, Uli and Stadler, Constanze and Stahnke, Svenja and Stiegler, Gertrud and Walser, Georg and Wambsganß, Mathias and Wellisch, Ulrich}, title = {Forschungsbericht 2014}, organization = {Hochschule Rosenheim}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-12317}, pages = {68}, year = {2015}, abstract = {Mit dem j{\"a}hrlich erscheinenden Forschungsbericht m{\"o}chte die Hochschule Rosenheim einen Einblick in ihre vielf{\"a}ltigen Projekte und Aktivit{\"a}ten der angewandten Forschung und Entwicklung geben. Im Forschungsbericht 2014 wird {\"u}ber Vorhaben im Jahr 2014 berichtet.}, language = {de} } @techreport{AckermannAngermeierAueretal.2014, author = {Ackermann, Timo and Angermeier, Martin and Auer, Veronika and Beneken, Gerd and Bernhardt, Andreas and Botsch, Rafael and B{\"u}cker, Dominikus and Hager, Ralf and Hauck-Bauer, Eva and Heigl, Martin and Hirschm{\"u}ller, Sebastian and H{\"o}llm{\"u}ller, Janett and Karlinger, Peter and K{\"o}ster, Heinrich and Krause, Harald and Kucich, Martin and Matthias, Kira and Patzl, Victoria and Pl{\"o}nnigs, Ren{\´e} and Posch, Georg and Schanda, Ulrich and Scheerer, Josua and Schlecht, Johannes and Schmidt, Jochen and Stichler, Markus and Uhl, Cornelius and Viehhauser, Peter and Weber, Gabriel and Wolf, Christopher and Zagler, Stefan and Zentgraf, Peter}, title = {Forschungsbericht 2013}, organization = {Hochschule Rosenheim}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:861-opus4-12324}, pages = {56}, year = {2014}, abstract = {Mit dem j{\"a}hrlich erscheinenden Forschungsbericht m{\"o}chte die Hochschule Rosenheim einen Einblick in ihre vielf{\"a}ltigen Projekte und Aktivit{\"a}ten der angewandten Forschung und Entwicklung geben. Im Forschungsbericht 2013 wird {\"u}ber Vorhaben im Jahr 2013 berichtet.}, language = {de} } @article{NeumayerStecherGrimmetal.2023, author = {Neumayer, Martin and Stecher, Dominik and Grimm, Sebastian and Maier, Andreas and B{\"u}cker, Dominikus and Schmidt, Jochen}, title = {Fault and anomaly detection in district heating substations: A survey on methodology and data sets}, series = {Energy}, volume = {276}, journal = {Energy}, doi = {10.1016/j.energy.2023.127569}, pages = {127569}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{AlasmarNeumayerBucker2024, author = {Alasmar, Odai and Neumayer, Martin and Bucker, Dominikus}, title = {Data Augmentation Technique for Dealing with Multi-Resolution Issues in Segmentation of Photovoltaic Systems in Aerial Imagery}, series = {2024 International Conference on Electrical, Computer and Energy Technologies (ICECET}, booktitle = {2024 International Conference on Electrical, Computer and Energy Technologies (ICECET}, publisher = {IEEE}, doi = {10.1109/ICECET61485.2024.10698167}, pages = {1 -- 6}, year = {2024}, abstract = {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.}, language = {en} } @article{StecherNeumayerRamachandranetal.2024, author = {Stecher, Dominik and Neumayer, Martin and Ramachandran, Adithya and Hort, Anastasia and Maier, Andreas and B{\"u}cker, Dominikus and Schmidt, Jochen}, title = {Creating a labelled district heating data set: From anomaly detection towards fault detection}, series = {Energy}, volume = {313}, journal = {Energy}, publisher = {Elsevier}, doi = {10.1016/j.energy.2024.134016}, pages = {134016}, year = {2024}, abstract = {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.}, language = {en} }