Cover Image

Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pereira, Lucas, Nair, Vineet Jagadeesan, Dias, Bruno, Morais, Hugo, Annaswamy, Anuradha
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Systems and Control Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets
by: Nair, Vineet Jagadeesan, et al.
Published: (2023)

A Hierarchical Local Electricity Market for a DER-rich Grid Edge
by: Nair, Vineet Jagadeesan, et al.
Published: (2021)

A game-theoretic, market-based approach to extract flexibility from distributed energy resources
by: Nair, Vineet Jagadeesan, et al.
Published: (2024)

Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
by: Authier, Jules, et al.
Published: (2023)

Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
by: Nair, Vineet Jagadeesan
Published: (2024)

A Reactive Power Market for the Future Grid
by: Potter, Adam, et al.
Published: (2021)

Adapt and Stabilize, Then Learn and Optimize: A New Approach to Adaptive LQR
by: Fisher, Peter A., et al.
Published: (2025)

Dynamic resource coordination can increase grid hosting capacity to support more renewables, storage, and electrified load growth
by: Nair, Vineet Jagadeesan, et al.
Published: (2026)

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
by: Nair, Vineet Jagadeesan, et al.
Published: (2024)

An Error-Based Safety Buffer for Safe Adaptive Control (Extended Version)
by: Fisher, Peter A., et al.
Published: (2025)

Resilient Constrained Reinforcement Learning
by: Ding, Dongsheng, et al.
Published: (2023)

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting
by: Garcia, Joaquim Dias, et al.
Published: (2021)

Employing Federated Learning for Training Autonomous HVAC Systems
by: Hagström, Fredrik, et al.
Published: (2024)

Towards Fast Rates for Federated and Multi-Task Reinforcement Learning
by: Zhu, Feng, et al.
Published: (2024)

Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
by: Zhang, Chenyu, et al.
Published: (2024)

Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
by: Li, Jinhao, et al.
Published: (2024)

Reliably-stabilizing piecewise-affine neural network controllers
by: Fabiani, Filippo, et al.
Published: (2021)

Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
by: Li, Jinhao, et al.
Published: (2024)

Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks
by: Christianson, Nicolas, et al.
Published: (2024)

Multiobjective optimization-based design and dispatch of islanded, hybrid microgrids for remote, off-grid communities in sub-Saharan Africa
by: Nair, Vineet Jagadeesan
Published: (2026)

Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee
by: Xiao, Chenhan, et al.
Published: (2023)

Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise
by: Kanakeri, Vinay, et al.
Published: (2025)

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

Learning to Sparsify Stochastic Linear Bandits
by: Wang, Zhengmiao, et al.
Published: (2026)

On the Foundation of Distributionally Robust Reinforcement Learning
by: Wang, Shengbo, et al.
Published: (2023)

Communication-Efficient Learning for Satellite Constellations
by: Tudose, Ruxandra-Stefania, et al.
Published: (2025)

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

Online Learning for Supervisory Switching Control
by: Sun, Haoyuan, et al.
Published: (2026)

Communication-Efficient Stochastic Distributed Learning
by: Ren, Xiaoxing, et al.
Published: (2025)

Integration Matters for Learning PDEs with Backward SDEs
by: Park, Sungje, et al.
Published: (2025)

Jointly Computation- and Communication-Efficient Distributed Learning
by: Ren, Xiaoxing, et al.
Published: (2025)

(Un)supervised Learning of Maximal Lyapunov Functions
by: Barreau, Matthieu, et al.
Published: (2024)

Learning Linear Dynamics from Bilinear Observations
by: Sattar, Yahya, et al.
Published: (2024)

Offline Reinforcement Learning via Inverse Optimization
by: Dimanidis, Ioannis, et al.
Published: (2025)

Robust Q-Learning under Corrupted Rewards
by: Maity, Sreejeet, et al.
Published: (2024)

Learning Exactly Linearizable Deep Dynamics Models
by: Moriyasu, Ryuta, et al.
Published: (2023)

Modular Distributed Nonconvex Learning with Error Feedback
by: Carnevale, Guido, et al.
Published: (2025)

Distributionally Robust Federated Learning with Outlier Resilience
by: Wang, Zifan, et al.
Published: (2025)

Offline Hierarchical Reinforcement Learning via Inverse Optimization
by: Schmidt, Carolin, et al.
Published: (2024)

Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning
by: Zhang, Runyu, et al.
Published: (2025)