Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
Highlights Reinforcement learning-based method to using a whole building energy model for HVAC optimal control. Implementation and deployment of the method in an existing novel heating system (Mullion system) of an office building. Building energy modeling for the Mullion system and sub-hourly multi-objective model calibration using the Bayesian method and genetic algorithm optimization. 16.7% heating demand reduction in the real-life deployment with more than 95% probability compared to the old rule-based control. Discussions on the practicability of the control method.
Abstract Whole building energy model (BEM) is a physics-based modeling method for building energy simulation. 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 heating, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computational speed limit its practical application in real-time HVAC optimal control. Therefore, this study proposes 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 energy 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.
Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
Highlights Reinforcement learning-based method to using a whole building energy model for HVAC optimal control. Implementation and deployment of the method in an existing novel heating system (Mullion system) of an office building. Building energy modeling for the Mullion system and sub-hourly multi-objective model calibration using the Bayesian method and genetic algorithm optimization. 16.7% heating demand reduction in the real-life deployment with more than 95% probability compared to the old rule-based control. Discussions on the practicability of the control method.
Abstract Whole building energy model (BEM) is a physics-based modeling method for building energy simulation. 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 heating, ventilation and air-conditioning (HVAC) systems. However, its high-order nature and slow computational speed limit its practical application in real-time HVAC optimal control. Therefore, this study proposes 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 energy 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.
Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning
Zhang, Zhiang (Autor:in) / Chong, Adrian (Autor:in) / Pan, Yuqi (Autor:in) / Zhang, Chenlu (Autor:in) / Lam, Khee Poh (Autor:in)
Energy and Buildings ; 199 ; 472-490
16.07.2019
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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