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Benchmarking Building Control Systems Thanks to Calibrated Models
Sponsored by CSEMJan 19 2021
Buildings play an important role in total energy consumption and worldwide greenhouse gas (GHG) emissions. Smart control strategies are key technological enablers for reducing the GHG footprints of buildings.
3,4 but their simple nature, combined with possible tuning errors, can lead to sub-optimal control behavior.
5 Automatized, efficient building control can significantly reduce energy consumption and emissions.
Research has been done into model predictive control (MPC),
6,7,8,9 adaptive or learning-based MPC
10 and reinforcement learning (RL)
11,12,13 but many of these studies have suffered from the non-standardized evaluation of their control performance.
Energym’s Contribution ....

New York , United States , Perifereia Ionion Nison , Ali Baba , Perifereia Kentrikis Makedonias , Emilia Romagna , M Mohd Radzi , L Canal Casals , B Schubnel Centre Suisse , P Scharnhorst Centre Suisse , International Workshop On Equation , P Taddeo Catalonia Institute For Energy Research , Cf Bandera University Of Navarra , International Modelica Conference , International Conference On Systems For Energy , Switzerlandy Stauffer Centre Suisse , T Gorecki Centre Suisse , International Conference On Control , International Conference , Oklahoma State University , Schneider Electric , Global Alliance For Buildings , J Salom Catalonia Institute For Energy Research , Antonis Peppas National Technical University Of Athens , Alibaba Inc , M Boegli Centre Suisse ,