12 citations · 18 across the 10 of their papers we have counts for
11 papers
Learning Control Policies from Heterogeneous Multi-Horizon Time Series in Battery Energy Management Systems
Sheng Yin, Vivek Teja Tanjavooru, Holger Hesse +1
This paper introduces Representation-to-Decision (R2D), an end-to-end imitation learning framework that maps heterogeneous multi-horizon time-series inputs directly to battery cont…
Very Exciting: Zero-Shot Model Predictive Control of Buildings via Excitation-Based Generalized Transfer Learning Models
Fabian Raisch, Felix Koch, Zack Xuereb Conti +2
The widespread adoption of data-driven, energy-efficient model predictive control (MPC) in buildings remains hindered by substantial effort to collect data and train models for ind…
Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
Jan Marco Ruiz de Vargas, Fabian Raisch, Zoltan Nagy +2
Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of predictive modeling and reinforc…
Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control
Fabian Raisch, Timo Germann, J. Nathan Kutz +3
Gray-box RC models are widely used to enable energy-efficient model predictive control (MPC) in buildings. However, estimating RC parameters remains difficult, as conventional opti…
Robust and Interpretable Graph Neural Networks for Power Systems State Estimation
Arbel Yaniv, Kilian Golinski, Christoph Goebel
This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing…
BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Ehimare Okoyomon, Christoph Goebel
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with gen…
Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon, Arbel Yaniv, Christoph Goebel
Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (G…
Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
Fabian Raisch, Max Langtry, Felix Koch +3
Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine…
Price Aware Power Split Control in Heterogeneous Battery Storage Systems
Sheng Yin, Vivek Teja Tanjavooru, Thomas Hamacher +2
This paper presents a unified framework for the optimal scheduling of battery dispatch and internal power allocation in Battery energy storage systems (BESS). This novel approach i…
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
Fabian Raisch, Thomas Krug, Christoph Goebel +1
Transfer Learning (TL) is an emerging field in modeling building thermal dynamics. This method reduces the data required for a data-driven model of a target building by leveraging…
Household Electricity Demand Forecasting -- Benchmarking State-of-the-Art Methods
Andreas Veit, Christoph Goebel, Rohit Tidke +2
The increasing use of renewable energy sources with variable output, such as solar photovoltaic and wind power generation, calls for Smart Grids that effectively manage flexible lo…