Copertina

Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yang, Mei, Liu, Gao Qiu andJunyong, Liu, Kai
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

Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains
di: Xia, Weijie, et al.
Pubblicazione: (2024)

Renewable energy exporting consumption-oriented transfer limit switching control: A unsupervised learning-based method
di: Qiu, Gao, et al.
Pubblicazione: (2024)

One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION
di: Donati, Cesare, et al.
Pubblicazione: (2023)

Managing power balance and reserve feasibility in the AC unit commitment problem
di: Parker, Robert, et al.
Pubblicazione: (2024)

Stochastic optimization for unit commitment applied to the security of supply: extended version
di: Dumas, Jonathan
Pubblicazione: (2023)

Deep multitask neural networks for solving some stochastic optimal control problems
di: Yeo, Christian
Pubblicazione: (2024)

The impact of sensor placement on graph-neural-network-based leakage detection
di: van Gemert, J. J. H., et al.
Pubblicazione: (2026)

Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
di: Pauli, Patricia, et al.
Pubblicazione: (2023)

Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
di: Wu, Long, et al.
Pubblicazione: (2024)

An active learning method for solving competitive multi-agent decision-making and control problems
di: Fabiani, Filippo, et al.
Pubblicazione: (2022)

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
di: Shen, Shan, et al.
Pubblicazione: (2025)

Physics-guided gated recurrent units for inversion-based feedforward control
di: Lin, Mingdao, et al.
Pubblicazione: (2025)

PowerGraph: A power grid benchmark dataset for graph neural networks
di: Varbella, Anna, et al.
Pubblicazione: (2024)

Shape error prediction in 5-axis machining using graph neural networks
di: Huuk, Julia, et al.
Pubblicazione: (2024)

Generating adversarial inputs for a graph neural network model of AC power flow
di: Parker, Robert
Pubblicazione: (2026)

Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
di: Coulombe, Marc-Antoine, et al.
Pubblicazione: (2025)

Learn2Drive: A neural network-based framework for socially compliant automated vehicle control
di: Liu, Yuhui, et al.
Pubblicazione: (2025)

HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores
di: Kirchgässner, Wilhelm, et al.
Pubblicazione: (2024)

Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
di: Zhang, Yadong, et al.
Pubblicazione: (2023)

Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems
di: Zhou, Ruikun, et al.
Pubblicazione: (2024)

Physics-guided denoiser network for enhanced additive manufacturing data quality
di: Halder, Pallock, et al.
Pubblicazione: (2025)

A neural network-based approach to hybrid systems identification for control
di: Fabiani, Filippo, et al.
Pubblicazione: (2024)

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

Principled Learning-to-Communicate with Quasi-Classical Information Structures
di: Liu, Xiangyu, et al.
Pubblicazione: (2026)

A Unified Approach for Learning the Dynamics of Power System Generators and Inverter-based Resources
di: Liu, Shaohui, et al.
Pubblicazione: (2024)

Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction
di: Jiang, Feilong, et al.
Pubblicazione: (2024)

On zero-shot learning in neural state estimation of power distribution systems
di: Berezin, Aleksandr, et al.
Pubblicazione: (2024)

Large-scale road network partitioning: a deep learning method based on convolutional autoencoder model
di: Xu, Pengfei, et al.
Pubblicazione: (2024)

Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
di: Fabiani, Filippo, et al.
Pubblicazione: (2022)

Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
di: Kai, Jian, et al.
Pubblicazione: (2025)

Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures
di: Zheng, Dongzhe, et al.
Pubblicazione: (2025)

Large width penalization for neural network-based prediction interval estimation
di: Amnuaypongsa, Worachit, et al.
Pubblicazione: (2024)

Structured physics-guided neural networks for electromagnetic commutation applied to industrial linear motors
di: Bolderman, Max, et al.
Pubblicazione: (2024)

Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
di: Liu, Zheng
Pubblicazione: (2026)

Probabilistic forecasting of power system imbalance using neural network-based ensembles
di: Van Gompel, Jonas, et al.
Pubblicazione: (2024)

Reliable Projection Based Unsupervised Learning for Semi-Definite QCQP with Application of Beamforming Optimization
di: Wang, Xiucheng, et al.
Pubblicazione: (2024)

Learning a Class of Mixed Linear Regressions: Global Convergence under General Data Conditions
di: Liu, Yujing, et al.
Pubblicazione: (2025)

A new graph-based surrogate model for rapid prediction of crashworthiness performance of vehicle panel components
di: Li, Haoran, et al.
Pubblicazione: (2025)

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

Learning and Current Prediction of PMSM Drive via Differential Neural Networks
di: Mei, Wenjie, et al.
Pubblicazione: (2024)