@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} } @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} }