@article{WenningerStecherSchmidt2019, author = {Wenninger, Marc and Stecher, Dominik and Schmidt, Jochen}, title = {SVM-Based Segmentation of Home Appliance Energy Measurements}, series = {Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019}, journal = {Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019}, pages = {1666 -- 1670}, year = {2019}, abstract = {Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change. Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information. We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households.}, language = {en} } @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} }