1 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Learning to Fix: Optimisation-Aware Machine Learning for Accelerated Unit Commitment
Benjamin Fritz, Andreas Makrides, Maryam Fetanat +1
Unit Commitment is a computationally demanding mixed-integer linear optimisation problem requiring many binary commitment decisions across a scheduling horizon. To reduce this comp…
Control-Oriented Scenario Tree Construction through Reinforcement Learning
Fabio Pavirani, Bert Claessens, Pierre Pinson +1
Multistage stochastic model predictive control (MPC) handles uncertainty by optimizing over a scenario tree, a finite branching approximation of future outcomes constructed from sa…
S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty
Fabio Pavirani, Bert Claessens, Pierre Pinson +1
Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance, optimizing the dispatch of…
Decision-calibrated prediction sets for robust power system operations
Akylas Stratigakos, Honglin Wen, Elina Spyrou +1
Robust optimization offers a tractable approach to balance operating costs and reliability in power systems dominated by weather-dependent renewable uncertainty, but its performanc…
Stabilizing distribution-free probabilistic forecasts
Jente Van Belle, Honglin Wen, Wouter Verbeke +1
Multi-step-ahead forecasts are often updated as new observations become available, since shorter forecast horizons typically improve forecast quality. However, such improvements co…