سال انتشار:
2020
عنوان انگلیسی مقاله:
Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
ترجمه فارسی عنوان مقاله:
یادگیری تقویتی عمیق برای بهینه سازی کنترل دمای داخلی و مصرف انرژی گرمایشی در ساخت
منبع:
Sciencedirect - Elsevier - Energy & Buildings, 224 (2020) 110225. doi:10.1016/j.enbuild.2020.110225
نویسنده:
Silvio Brandi a, Marco Savino Piscitelli a, Marco Martellacci b, Alfonso Capozzoli a,⇑
چکیده انگلیسی:
In this work, Deep Reinforcement Learning (DRL) is implemented to control the supply water temperature
setpoint to terminal units of a heating system. The experiment was carried out for an office building
in an integrated simulation environment. A sensitivity analysis is carried out on relevant hyperparameters
to identify their optimal configuration. Moreover, two sets of input variables were considered for
assessing their impact on the adaptability capabilities of the DRL controller. In this context a static and
dynamic deployment of the DRL controller is performed. The trained control agent is tested for four different
scenarios to determine its adaptability to the variation of forcing variables such as weather conditions,
occupant presence patterns and different indoor temperature setpoint requirements. The
performance of the agent is evaluated against a reference controller that implements a combination of
rule-based and climatic-based logics. As a result, when the set of variables are adequately selected a heating
energy saving ranging between 5 and 12% is obtained with an enhanced indoor temperature control
with both static and dynamic deployment. Eventually the study proves that if the set of input variables
are not carefully selected a dynamic deployment is strictly required for obtaining good performance.
Keywords: Deep reinforcement learning | Building adaptive control | Energy efficiency | Temperature control | HVAC
قیمت: رایگان
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