دانلود مقاله انگلیسی رایگان:یادگیری تقویتی عمیق برای بهینه سازی کنترل دمای داخلی و مصرف انرژی گرمایشی در ساخت - 2020
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  • Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings
    Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings

    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


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 19
    حجم فایل: 3970 کیلوبایت

    قیمت: رایگان


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