Deep Reinforcement Learning for Temperature Control in Buildings and Adversarial Attacks

Deep Reinforcement Learning for Temperature Control in Buildings and Adversarial Attacks

Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are energy consuming and traditional methods used for building control results in energy losses. The methods cannot account for non-linear dependencies in the thermal behaviour. Deep Reinforcement Learning (DRL) is a powerful meth...

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Bibliographic Details
Main Author: Ammouri, Kevin
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2021
Subjects:
Deep Reinforcement Learning
Adversarial Attacks
Optimal Attacks
Building Control
Optimal Control
Energy Efficiency
Djup förstärkande inlärning
Adversarial Attacker
Optimala Attacker
Byggnadskontroll
Optimal Kontroll
Energieffektivitet
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301052

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301052

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