Copertina

CommonPower: A Framework for Safe Data-Driven Smart Grid Control

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Eichelbeck, Michael, Markgraf, Hannah, Althoff, Matthias
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Systems and Control Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Contingency-constrained economic dispatch with safe reinforcement learning
di: Eichelbeck, Michael, et al.
Pubblicazione: (2022)

Improving Stochastic Action-Constrained Reinforcement Learning via Truncated Distributions
di: Stolz, Roland, et al.
Pubblicazione: (2025)

Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
di: Lützow, Laura, et al.
Pubblicazione: (2025)

Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking
di: Stolz, Roland, et al.
Pubblicazione: (2024)

To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control
di: Schulte, Victor, et al.
Pubblicazione: (2026)

Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
di: Krasowski, Hanna, et al.
Pubblicazione: (2024)

PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects
di: Markgraf, Hannah, et al.
Pubblicazione: (2025)

Data-Driven Permissible Safe Control with Barrier Certificates
di: Mazouz, Rayan, et al.
Pubblicazione: (2024)

Synergies between Federated Foundation Models and Smart Power Grids
di: Hosseinalipour, Seyyedali, et al.
Pubblicazione: (2025)

Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data
di: Li, Yunfeng, et al.
Pubblicazione: (2025)

A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
di: Sun, Yixuan, et al.
Pubblicazione: (2024)

Leveraging Analytic Gradients in Provably Safe Reinforcement Learning
di: Walter, Tim, et al.
Pubblicazione: (2025)

Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach
di: Debelle, Thomas, et al.
Pubblicazione: (2025)

Data-Driven Prediction and Control of Hammerstein-Wiener Systems with Implicit Gaussian Processes
di: Yin, Mingzhou, et al.
Pubblicazione: (2025)

BSAT: B-Spline Adaptive Tokenizer for Long-Term Time Series Forecasting
di: Reinwardt, Maximilian, et al.
Pubblicazione: (2026)

Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids
di: Dogoulis, Pantelis, et al.
Pubblicazione: (2026)

Training Verifiably Robust Agents Using Set-Based Reinforcement Learning
di: Wendl, Manuel, et al.
Pubblicazione: (2024)

Falsification-driven reinforcement learning for maritime motion planning
di: Müller, Marlon, et al.
Pubblicazione: (2025)

State and Action Factorization in Power Grids
di: Losapio, Gianvito, et al.
Pubblicazione: (2024)

Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure
di: Ladner, Tobias, et al.
Pubblicazione: (2024)

Multi-Objective Reinforcement Learning for Power Grid Topology Control
di: Lautenbacher, Thomas, et al.
Pubblicazione: (2025)

Multilayer GNN for Predictive Maintenance and Clustering in Power Grids
di: Kazim, Muhammad, et al.
Pubblicazione: (2025)

Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning
di: Nauck, Christian, et al.
Pubblicazione: (2024)

Safe Online Control-Informed Learning
di: Zhou, Tianyu, et al.
Pubblicazione: (2025)

A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids
di: Chifu, Viorica Rozina, et al.
Pubblicazione: (2024)

Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment
di: Peter, Benjamin M., et al.
Pubblicazione: (2025)

PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
di: Suri, Dhruv, et al.
Pubblicazione: (2025)

Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids
di: Balada, Christoph, et al.
Pubblicazione: (2022)

Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids
di: Gillioz, Marc, et al.
Pubblicazione: (2026)

Data-Driven Adversarial Online Control for Unknown Linear Systems
di: Liu, Zishun, et al.
Pubblicazione: (2023)

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids
di: Tiwari, Deepak, et al.
Pubblicazione: (2024)

Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach
di: Zhang, Zecheng
Pubblicazione: (2024)

Optimal Power Grid Operations with Foundation Models
di: Puech, Alban, et al.
Pubblicazione: (2024)

Myopically Verifiable Probabilistic Certificates for Safe Control and Learning
di: Wang, Zhuoyuan, et al.
Pubblicazione: (2024)

Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
di: Hoischen, Nicolas, et al.
Pubblicazione: (2024)

Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
di: Manyari, Yassine El, et al.
Pubblicazione: (2025)

Safe Reinforcement Learning using Action Projection: Safeguard the Policy or the Environment?
di: Markgraf, Hannah, et al.
Pubblicazione: (2025)

Direct Data Driven Control Using Noisy Measurements
di: Esmzad, Ramin, et al.
Pubblicazione: (2025)

Safe and Near-Optimal Control with Online Dynamics Learning
di: Prajapat, Manish, et al.
Pubblicazione: (2025)

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
di: de Jong, Thomas, et al.
Pubblicazione: (2024)