سال انتشار:
2020
عنوان انگلیسی مقاله:
Energy-Efficient Heating Control for Smart Buildings with Deep Reinforcement Learning
ترجمه فارسی عنوان مقاله:
کنترل گرمای انرژی کارامد برای ساختمانهای هوشمند با یادگیری تقویتی عمیق
منبع:
Sciencedirect - Elsevier - Journal of Building Engineering, Journal Pre-proof, 101739. doi:10.1016/j.jobe.2020.101739
نویسنده:
Anchal Gupta, Youakim Badr, Ashkan Negahban, Robin G. Qiu
چکیده انگلیسی:
Buildings account for roughly 40% of the total energy consumption in the world, out of which
heating, ventilation, and air conditioning are the major contributors. Traditional heating controllers
are inecient due to lack of adaptability to dynamic conditions such as changing user
preferences and outside temperature patterns. Therefore, it is necessary to design energy-ecient
controllers that can improvise occupant thermal comfort (deviation from setpoint temperature)
while reducing energy consumption. This research presents a Deep Reinforcement Learning
(DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart
buildings. We perform extensive simulation experiments using real-world outside temperature
data. The results show that the DRL-based smart controller outperforms a traditional thermostat
controller by improving thermal comfort between 15% - 30% and reducing energy costs between
5% - 12% in the simulated environment. A second set of experiments is then performed for the
case of multiple buildings, each having its own heating equipment. The performance is compared
when the buildings are controlled centrally (using a single DRL-based controller) versus
decentralized control, where each heater is controlled independently and has its own DRL-based
controller. We observe that as the number of buildings and dierences in their setpoint temperatures
increase, decentralized control performs better than a centralized controller. The results
have practical implications for heating control, especially in areas with multiple buildings such
as residential complexes with multiple houses.
Keywords : Deep reinforcement learning | Simulation | Occupant thermal comfort | Heating controller | HVAC
قیمت: رایگان
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