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Managing Ventilation Systems for Improving User Comfort in Smart Buildings Using Reinforcement Learning Agents

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In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, PhD Session, 2014.
With the fast development of information technology and increasingly prominent environmental problems, building comfort and energy management become the major tasks for an intelligent residential building system. This paper identifies the system requirements of Smart Buildings, analyzes the problems that need to be solved and how Reinforcement Learning is suitable for dealing with them. It also proposes to represent parts of Smart Buildings as Cyber- Physical Systems. Although the global goal is to model and manage a complex and whole system of a Smart Building, since the work is in progress, in this paper we mainly focus on how Reinforcement Learning technique is good at controlling subsystems, specifically the Ventilation System. The experimental results show the advantages of our system compared with the widely used baselines: on/off control and PI control approaches.
energy, smart buildings, reinforcement learning, multi-agent system, cyber-physical system
Publication Category:
International conference with proceedings
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