12 citations · 18 across the 10 of their papers we have counts for
6 papers · 1 filter
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…