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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 ....

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 ,

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.

Jonathan May , Ralph Weischedel , Supervisory Computer Scientist Ralph Weischedel , Xusen Yin , Deep Reinforcement Learning , ஜொனாதன் இருக்கலாம் , ஆழமான வலுவூட்டல் கற்றல் ,

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
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Michael Everett , Ford Motor Company , Department Of Aeronautics , Transactions On Neural Networks , Certified Adversarial Robustness , Deep Reinforcement , Neural Networks , Astro Professor Jonathan , Jonathan How , Bjorn Lutjens , Mit Aeroastro , Deep Reinforcement Learning , Adversarial Attacks , Autonomous Vehicle Navigation , Collision Avoidance , மைக்கேல் எவரெட் , ஃபோர்ட் மோட்டார் நிறுவனம் , துறை ஆஃப் வான்செலவுத்துறை , பரிவர்த்தனைகள் ஆன் நரம்பியல் நெட்வொர்க்குகள் , ஆழமான வலுவூட்டல் , நரம்பியல் நெட்வொர்க்குகள் , ஆஸ்ட்ரோ ப்ரொஃபெஸர் ஜொனாதன் , ஜொனாதன் எப்படி , ஆழமான வலுவூட்டல் கற்றல் , தன்னாட்சி வாகனம் வழிசெலுத்தல் ,