TY - JOUR A1 - Wenninger, Marc A1 - Maier, Andreas A1 - Schmidt, Jochen T1 - DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany JF - Scientific Data N2 - Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of. KW - Machine Learning Y1 - 2021 UR - https://doi.org/10.1038/s41597-021-00963-2 VL - 8 IS - 176 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 - JOUR A1 - Höpfl, Felix A1 - Peisl, Thomas A1 - Greiner, Christian T1 - Exploring stakeholder perspectives: Enhancing robot acceptance for sustainable healthcare solutions JF - Sustainable Technology and Entrepreneurship N2 - The pandemic has highlighted the fact that healthcare systems around the world are under pressure. Demographic change is leading to an increasing shortage of care workers in most countries, and the demographic challenge is only just beginning in most societies. While robots are widely used in industry, robotic support in healthcare is still limited to very specialized robots in the operating theatre. The question of what type of deployment is likely to be successful in a healthcare scenario is not only a technological or economical question but also one of technology acceptance. The answer to this question supports entrepreneurial opportunities to develop sustainable healthcare solutions. In this paper, we analyze the acceptance of robots in elderly care from the perspective of patients, patient families, and geriatric care professionals. To understand the various positions and to identify the suitability of existing acceptance models, we applied stakeholder mapping to conduct qualitative interviews with 14 people with different knowledge backgrounds and levels of involvement in care situations, based on 9 videos showing different robots and application scenarios. The results confirmed that existing technology acceptance models need to be extended by factors such as robot appearance. We found that the background knowledge of the respondents influences the results of the questions about e.g. safety concerns. In addition, we found that the contribution to patients' self-determination and independence is an important factor that is not included in existing technology acceptance models. Finally, the discovery of a significant discrepancy between the self-perception and the external perception of the different stakeholders regarding the acceptance of a service robot can be explained by the stakeholder positions involved in caring for the benefit of a specific patient. These findings encourage further research, especially with the underlying assumption that technology acceptance in healthcare is not just a patient issue, but a stakeholder issue. Stakeholder mapping is a valid tool to analyze the interdependencies for the acceptance of robots. Therefore, we suggest using a tool such as stakeholder mapping to further analyze these issues. KW - Technology acceptance KW - Robot acceptance KW - Stakeholder mapping KW - Geriatrics Y1 - 2023 U6 - https://doi.org/10.1016/j.stae.2023.100045 VL - 2 IS - 3 SP - 100045 ER - 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 - JOUR A1 - Mayr, Ilona A1 - Rößle, Felix T1 - Financial Performance of Mid-and High-Tech Industries in Crisis and Non-Crisis Periods JF - Current Issues of Business and Law N2 - Investments in research and development (R&D) are crucial for innovation and economic growth, yet dis-tinctions between high-tech and mid-tech industries may lead tovarying financial outcomes. Building onFuest et al.(2024), this study examines the financial performance of publicly listed high-and mid-techcompanies from a shareholder'sperspective. Using data from theEU Industrial R&D Investment Scoreboard and LSEGWorkspace for 2,349 listed companies, market-capitalization-weighted portfolios were constructed and benchmarked against global and local market indices. Performance was evaluated using theSharpe ratio and multi-factor asset pricing models (Carhart four-factor and Fama-French five-factor), distinguishing between crisis and non-crisis periods, as well as regional and sectoral variations. Results showthat, at the aggregate level,high-tech portfolios consistentlyand significantly outperform mid-tech and "other" categories on absolute and risk-adjusted bases. Mid-tech indus-tries exhibitresilience during crisis but lower alphasthan high-techindustries,and sectoral heterogeneity high-lightsdiversification benefits. Furthermore, theanalysis reveals investment opportunities in R&D-intensive sec-tors, emphasizing crisis resilience and regional competitiveness. KW - Financial performance KW - mid-tech and high-tech industries Y1 - 2026 UR - https://cibljournal.com/index.php/cibl/article/view/23/13 VL - 2025 IS - 2(9) SP - 14 EP - 26 ER -