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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 yorkUnited statesPerifereia ionion nisonAli babaPerifereia kentrikis makedoniasEmilia romagnaM mohd radziL canal casalsB schubnel centre suisseP scharnhorst centre suisseInternational workshop on equationP taddeo catalonia institute for energy researchCf bandera university of navarraInternational modelica conferenceInternational conference on systems for energySwitzerlandy stauffer centre suisse