A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modelling building HVAC control strategies using a deep reinforcement learning approach
https://orcid.org/0009-0005-0125-4162
https://orcid.org/0000-0003-4442-3390
Abstract Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort.
Highlights Our model-free DRL outperforms others in speed and error reduction. Our model reduces energy use by 2-14% and enhancing comfort. Our method also outperforms PPO, speeding up convergence by up to 66%. Two reward setups tested to assess their impact.
Modelling building HVAC control strategies using a deep reinforcement learning approach
https://orcid.org/0009-0005-0125-4162
https://orcid.org/0000-0003-4442-3390
Abstract Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort.
Highlights Our model-free DRL outperforms others in speed and error reduction. Our model reduces energy use by 2-14% and enhancing comfort. Our method also outperforms PPO, speeding up convergence by up to 66%. Two reward setups tested to assess their impact.
Modelling building HVAC control strategies using a deep reinforcement learning approach
https://orcid.org/0009-0005-0125-4162
https://orcid.org/0000-0003-4442-3390
Abstract Heating, ventilation, and air-conditioning (HVAC) systems are responsible for a considerable proportion of total building energy consumption but are also vital for improved indoor temperature comfort, indoor air quality and well-being of building occupants. Thus, developing control strategies for HVAC systems is critical for the total life cycle of any building projects. Particularly, HVAC and building operations are not stationary but are filled with fuelled by environmental dynamisms and unexpected disruptions such as users' activities, weather conditions, occupancy rate, and operation of machinery and systems. This research aims to develop and propose a strategic control learning framework for HVAC systems using the deep reinforcement learning (DRL) approach. The results show that the proposed Phasic Policy Gradient (PPG) based method is more adaptive to changes in real building's environments. Notably, PPG performs better and more reliable than the conventional method for HVAC control optimization with about 2-14% in energy consumption reduction and indoor temperature comfort enhancement, along with a 66% faster convergence rate. Overall, our findings demonstrate that our proposed DRL approach is less resource intensive and much easier than the conventional approach in deriving solutions for HVAC control optimization driven by energy efficiency and indoor temperature comfort.
Highlights Our model-free DRL outperforms others in speed and error reduction. Our model reduces energy use by 2-14% and enhancing comfort. Our method also outperforms PPO, speeding up convergence by up to 66%. Two reward setups tested to assess their impact.
Modelling building HVAC control strategies using a deep reinforcement learning approach
Nguyen, Anh Tuan (author) / Pham, Duy Hoang (author) / Oo, Bee Lan (author) / Santamouris, Mattheos (author) / Ahn, Yonghan (author) / Lim, Benson T.H. (author)
Energy and Buildings ; 310
2024-03-05
Article (Journal)
Electronic Resource
English
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