TY - JOUR A1 - Stecher, Dominik A1 - Ziegltrum, Lukas A1 - Reiprich, Paul A1 - Fuchs, Christian A1 - Maier, Andreas A1 - Schmidt, Jochen T1 - Neural network synthetic dataset generation for fault detection in district heating substations JF - Smart Energy N2 - District heating systems (DHS) play a vital role in sustainable heating solutions and the decarbonization of the energy sector. However, inefficiencies due to undetected faults in substations result in high return temperatures, increasing heat losses, and limiting the integration of renewable energy sources. The lack of publicly available labeled datasets poses a significant challenge for fault detection using supervised learning models. To address this issue, this study explores three machine learning-based synthetic data generation techniques – time series forecasting, generative adversarial networks (GANs), and fault signature transfer. These methods aim to increase publicly available data either by sharing the generating model or a synthetic dataset. The novelty lies in the combination of advanced supervised machine learning methods being applied to a large, fully labeled data set to create new, equally labeled data for publication, as, to our knowledge, no such dataset has been compiled before. We evaluate our methods on the first-of-its-kind ILSE dataset, which includes real-world smart meter data from 547 substations and 1,162 reviewed faults from a German DHS network, including detailed root cause information. Overall, time series forecasting achieves an MAPE of 3% to 10% for inlet and outlet temperature and 25% to 40% for heat load and flow rate, both of which are within year-to-year variance. For GANs, specifically TimeGAN, we found a discriminative score of about 0.10 compared to 0.24 in the original publication when tested on Energy benchmark data. Fault signature transfer has yet to yield usable results, most likely due to the high variance in the fault signatures, fault duration, and overlapping or multiple root causes. Finally, fault data in the synthetic data is not yet good enough for practical use, e.g. training a fault detector. KW - Synthetic fault generation KW - Data augmentation KW - District heating Y1 - 2025 U6 - https://doi.org/10.1016/j.segy.2025.100206 VL - 20 SP - 100206 PB - Elsevier ER - TY - CHAP A1 - Hamberger, Anna A1 - Murgul, Sebastian A1 - Schmidt, Jochen A1 - Heizmann, Michael T1 - Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription T2 - Proceedings of the 50th International Computer Music Conference 2025 N2 - Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription. KW - Machine Learning Y1 - 2025 SP - 438 EP - 445 PB - The International Computer Music Association ER -