22 citations · 34 across the 39 of their papers we have counts for
15 papers · 1 filter
Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow
Changhun Kim, Timon Conrad, Redwanul Karim +7
Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imply physically consistent sol…
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4
The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional rela…
A Standardized Framework for Machine Learning in Power System Protection
Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4
Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends strongly on the evaluation setting. Prote…
Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
Adarsh Bhandary Panambur, Siming Bayer, Andreas Maier
Enhancing classification performance in mammography remains a persistent challenge across both small curated datasets and large-scale clinical cohorts. Conventional transfer learni…
A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features
Adithya Ramachandran, Satyaki Chatterjee, Thorkil Flensmark B. Neergaard +3
District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse…
Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Prediction
Redwanul Karim, Changhun Kim, Timon Conrad +7
Accurate AC power flow (AC-PF) prediction under domain shift is critical when models trained on medium-voltage (MV) grids are deployed on high-voltage (HV) networks. Existing physi…
Robustness Evaluation of Machine Learning Models for Fault Classification and Localization In Power System Protection
Julian Oelhaf, Mehran Pashaei, Georg Kordowich +4
The growing penetration of renewable and distributed generation is transforming power systems and challenging conventional protection schemes that rely on fixed settings and local…
A Scoping Review of Machine Learning Applications in Power System Protection and Disturbance Management
Julian Oelhaf, Georg Kordowich, Mehran Pashaei +4
The integration of renewable and distributed energy resources reshapes modern power systems, challenging conventional protection schemes. This scoping review synthesizes recent lit…
Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Tomás Arias-Vergara +2
Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate c…
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim, Timon Conrad, Redwanul Karim +6
Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of sce…
Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
Julian Oelhaf, Georg Kordowich, Andreas Maier +3
The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially during fault events. However, the…
Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim +4
Germany's transition to a renewable energy-based power system is reshaping grid operations, requiring advanced monitoring and control to manage decentralized generation. Machine le…
Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Andreas Maier +1
Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while cont…
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
Adithya Ramachandran, Satyaki Chatterjee, Siming Bayer +2
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an un…
Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model
Satyaki Chatterjee, Siming Bayer, Andreas Maier
In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable. As a consequence, an acc…