Autonomous Thermal Control System
نویسنده
چکیده
Building heating and cooling accounts for nearly 18-24% of all energy usage. Building energy management can reduce both its operating costs and its carbon footprint. We propose the use of a factored Partially Observable Markov Decision Process (POMDP) to model and efficiently control a commercial building heating system. We use supervised learning and ground truth data to capture the parameters of the POMDP. We show that our solution outperforms the HVAC system in terms of energy efficiency.
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تاریخ انتشار 2010