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Found 5 papers, 2 papers with code
CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics
Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters.
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy.
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes
The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty.
Data-Driven Stochastic AC-OPF using Gaussian Processes
Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies.
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems.
Distributed Optimization
Optimization and Control
