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Benchmarking Building Control Systems Thanks to Calibrated Models

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

How to each AI decision-making skills and common sense: Play games

How to each AI decision-making skills and common sense: Play games
scienceblog.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from scienceblog.com Daily Mail and Mail on Sunday newspapers.

Algorithm helps artificial intelligence systems dodge adversarial inputs | MIT News | Massachusetts Institute of Technology

Caption: A deep-learning algorithm developed by MIT researchers is designed to help machines navigate in the real world. In tests with the video game Pong, the researchers introduced an “adversary” that pulled the ball slightly further down than it actually was. Credits: Image: Jose-Luis Olivares, MIT Terms of Use: Images for download on the MIT News office website are made available to non-commercial entities, press and the general public under a Creative Commons Attribution Non-Commercial No Derivatives license. You may not alter the images provided, other than to crop them to size. A credit line must be used when reproducing images; if one is not provided

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