A machine-learning-based event-triggered model predictive control for building energy management
Model predictive control (MPC) for building energy management has exhibited a huge potential for largely cutting down energy use and improving human comfort. However, the high demand for computational power for solving the optimization is challenging the widespread deployment of MPC in real building...
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sg-ntu-dr.10356-1690172023-06-27T02:54:40Z A machine-learning-based event-triggered model predictive control for building energy management Yang, Shiyu Chen, Wanyu Wan, Man Pun School of Mechanical and Aerospace Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Mechanical engineering Event-Triggered Mechanism Event-Triggered Mechanism Model predictive control (MPC) for building energy management has exhibited a huge potential for largely cutting down energy use and improving human comfort. However, the high demand for computational power for solving the optimization is challenging the widespread deployment of MPC in real buildings. Typical MPC employs a time-triggered mechanism (TTM) that runs optimization recurrently at each time step without considering the necessity, which could cause excessive computation resource usage. This paper proposes an event-triggered mechanism (ETM) that only runs optimization when triggering events occur. Contrary to conventional ETMs that only considers the current information (e.g., room conditions), the ETM proposed is based on a cost function that covers the past, current, and future information. Based on the proposed ETM, a machine-learning-based event-triggered model predictive control (ETMPC) system that optimizes both building energy efficiency and thermal comfort is developed. The developed ETMPC system is then employed for air-conditioning control for performance evaluation through simulations. The control performance of the proposed ETMPC is compared to an MPC employing TTM and a common thermostat. Compared to the MPC employing TTM, the proposed ETMPC reduced the computational load in terms of the number of optimization runs by 77.6%–88.2%, meanwhile, achieving similar energy savings (more than 9.3% energy saving over the thermostat) as the MPC employing TTM does (9% energy saving over the thermostat). With the significant reduction of computational load, the ETMPC achieves similar thermal comfort performance as the MPC employing TTM does, and it is significantly better over the thermostat. National Research Foundation (NRF) This research is supported by the National Research Foundation (NRF) of Singapore through grant no. NRF2016IDM-TRANS001-031. 2023-06-27T02:54:40Z 2023-06-27T02:54:40Z 2023 Journal Article Yang, S., Chen, W. & Wan, M. P. (2023). A machine-learning-based event-triggered model predictive control for building energy management. Building and Environment, 233, 110101-. https://dx.doi.org/10.1016/j.buildenv.2023.110101 0360-1323 https://hdl.handle.net/10356/169017 10.1016/j.buildenv.2023.110101 2-s2.0-85148325600 233 110101 en NRF2016IDM-TRANS001-031 Building and Environment © 2023 Elsevier Ltd. All rights reserved. |
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Engineering::Mechanical engineering Event-Triggered Mechanism Event-Triggered Mechanism Yang, Shiyu Chen, Wanyu Wan, Man Pun A machine-learning-based event-triggered model predictive control for building energy management |
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Model predictive control (MPC) for building energy management has exhibited a huge potential for largely cutting down energy use and improving human comfort. However, the high demand for computational power for solving the optimization is challenging the widespread deployment of MPC in real buildings. Typical MPC employs a time-triggered mechanism (TTM) that runs optimization recurrently at each time step without considering the necessity, which could cause excessive computation resource usage. This paper proposes an event-triggered mechanism (ETM) that only runs optimization when triggering events occur. Contrary to conventional ETMs that only considers the current information (e.g., room conditions), the ETM proposed is based on a cost function that covers the past, current, and future information. Based on the proposed ETM, a machine-learning-based event-triggered model predictive control (ETMPC) system that optimizes both building energy efficiency and thermal comfort is developed. The developed ETMPC system is then employed for air-conditioning control for performance evaluation through simulations. The control performance of the proposed ETMPC is compared to an MPC employing TTM and a common thermostat. Compared to the MPC employing TTM, the proposed ETMPC reduced the computational load in terms of the number of optimization runs by 77.6%–88.2%, meanwhile, achieving similar energy savings (more than 9.3% energy saving over the thermostat) as the MPC employing TTM does (9% energy saving over the thermostat). With the significant reduction of computational load, the ETMPC achieves similar thermal comfort performance as the MPC employing TTM does, and it is significantly better over the thermostat. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Yang, Shiyu Chen, Wanyu Wan, Man Pun |
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Article |
author |
Yang, Shiyu Chen, Wanyu Wan, Man Pun |
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Yang, Shiyu |
title |
A machine-learning-based event-triggered model predictive control for building energy management |
title_short |
A machine-learning-based event-triggered model predictive control for building energy management |
title_full |
A machine-learning-based event-triggered model predictive control for building energy management |
title_fullStr |
A machine-learning-based event-triggered model predictive control for building energy management |
title_full_unstemmed |
A machine-learning-based event-triggered model predictive control for building energy management |
title_sort |
machine-learning-based event-triggered model predictive control for building energy management |
publishDate |
2023 |
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https://hdl.handle.net/10356/169017 |
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1772828831453806592 |