دانلود مقاله انگلیسی رایگان:مدل انرژی کامل ساختمان برای کنترل بهینه HVAC: یک چارچوب عملی مبتنی بر یادگیری تقویتی عمیق - 2019
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  • Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
    Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning


    ترجمه فارسی عنوان مقاله:

    مدل انرژی کامل ساختمان برای کنترل بهینه HVAC: یک چارچوب عملی مبتنی بر یادگیری تقویتی عمیق


    منبع:

    Sciencedirect - Elsevier - Energy & Buildings, 199 (2019) 472-490: doi:10:1016/j:enbuild:2019:07:029


    نویسنده:

    Zhiang Zhang a , ∗, Adrian Chong b , Yuqi Pan c , Chenlu Zhang a , Khee Poh Lam a , b


    چکیده انگلیسی:

    Whole building energy model (BEM) is a physics-based modeling method for building energy simula- tion. It has been widely used in the building industry for code compliance, building design optimization, retrofit analysis, and other uses. Recent research also indicates its strong potential for the control of heat- ing, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computa- tional speed limit its practical application in real-time HVAC optimal control. Therefore, this study pro- poses a practical control framework (named BEM-DRL) that is based on deep reinforcement learning. The framework is implemented and assessed in a novel radiant heating system in an existing office building as a case study. The complete implementation process is presented in this study, including: building en- ergy modeling for the novel heating system, multi-objective BEM calibration using the Bayesian method and the Genetic Algorithm, deep reinforcement learning training and simulation results evaluation, and control deployment. By analyzing the real-life control deployment data, it is found that BEM-DRL achieves 16.7% heating demand reduction with more than 95% probability compared to the old rule-based control. However, the framework still faces the practical challenges including building energy modeling of novel HVAC systems and multi-objective model calibration. Systematic study is also needed for the design of deep reinforcement learning training to provide a guideline for practitioners.
    Keywords: HVAC | Energy efficiency | Whole building energy model | Optimal control | Deep reinforcement learning


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 19
    حجم فایل: 3147 کیلوبایت

    قیمت: رایگان


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