MO2.B.3
Reinforcement Learning-Guided Economic MPC for Scheduling Industrial Refrigeration
Maximilian Bloor, Imperial College London, United Kingdom; Cameron Wonchoba, Richard Pattison, Crossnokaye, United States; Ehecatl Antonio del Rio Chanona, Calvin Tsay, Imperial College London, United Kingdom
Session:
MO2.B: Learning Based Control and AI for Engery Operations Oral
Track:
Track
Location:
Finger Rock
Presentation Time:
Mon, 11 Jan, 16:40 - 17:00 MST (UTC -7)
Presentation
Discussion
Resources
No resources available.
Session MO2.B
MO2.B.1: Reinforcement Learning for Nonlinear Process Control: Stability, Robustness, and Practical Implementation
Zhe Wu, Xinji Zhu, National University of Singapore, Singapore; Panagiotis Christofides, University of California, Los Angeles, United States
MO2.B.2: Acquisition Functions for Gaussian Process-Based Model Predictive Control
Tai Xuan Tan, Florian Ludwig, RWTH Aachen University, Germany; Eike Cramer, University College London, United Kingdom
MO2.B.3: Reinforcement Learning-Guided Economic MPC for Scheduling Industrial Refrigeration
Maximilian Bloor, Imperial College London, United Kingdom; Cameron Wonchoba, Richard Pattison, Crossnokaye, United States; Ehecatl Antonio del Rio Chanona, Calvin Tsay, Imperial College London, United Kingdom
MO2.B.4: EFFICIENT ENMPC FOR DEMAND-SIDE MANAGEMENT OF DIRECT AIR CAPTURE USING REINFORCEMENT LEARNING
Sebastian Henzler, Daniel Ast, Daniel Rezo, Patrik Postweiler, Niklas von der Assen, RWTH Aachen University, Germany; Alexander Mitsos, JARA-ENERGY, Germany
MO2.B.5: Industrial electrification in the era of data centers: A Bayesian Optimization approach for grid-aware large load allocation
Jiyong Lee, Erhan Kutanoglu, Michael Baldea, Ilias Mitrai, The University of Texas at Austin, United States
MO2.B.6: Adaptive Risk-Aware Rolling-Horizon Microgrid Operations with Contextual Learning
Halil Iseri, Funda Iseri, Eleftherios Iakovou, Efstratios N. Pistikopoulos, Texas A&M University, United States
Resources
No resources available.