Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems
Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting th...
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sg-ntu-dr.10356-1603002022-07-19T02:25:43Z Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems Yang, Shiyu Wan, Man Pun Ng, Bing Feng Dubey, Swapnil Henze, Gregor P. Chen, Wanyu Baskaran, Krishnamoorthy School of Mechanical and Aerospace Engineering Energy Research Institute @ NTU (ERI@N) Engineering::Mechanical engineering Model Predictive Control Coordinated Control Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building's overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (−0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side. Building and Construction Authority (BCA) National Research Foundation (NRF) This research is supported by the National Research Foundation (NRF) of Singapore through the Building and Construction Authority (BCA) under the Green Buildings Innovation Cluster (GBIC) grant nos. NRF2015ENC-GBICRD001-020 and GBIC-R&D/DCP02. 2022-07-19T02:25:43Z 2022-07-19T02:25:43Z 2021 Journal Article Yang, S., Wan, M. P., Ng, B. F., Dubey, S., Henze, G. P., Chen, W. & Baskaran, K. (2021). Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems. Applied Energy, 297, 117112-. https://dx.doi.org/10.1016/j.apenergy.2021.117112 0306-2619 https://hdl.handle.net/10356/160300 10.1016/j.apenergy.2021.117112 2-s2.0-85107134486 297 117112 en NRF2015ENC-GBICRD001-020 GBIC-R&D/DCP02 Applied Energy © 2021 Elsevier Ltd. All rights reserved. |
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Engineering::Mechanical engineering Model Predictive Control Coordinated Control Yang, Shiyu Wan, Man Pun Ng, Bing Feng Dubey, Swapnil Henze, Gregor P. Chen, Wanyu Baskaran, Krishnamoorthy Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
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Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building's overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (−0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Yang, Shiyu Wan, Man Pun Ng, Bing Feng Dubey, Swapnil Henze, Gregor P. Chen, Wanyu Baskaran, Krishnamoorthy |
format |
Article |
author |
Yang, Shiyu Wan, Man Pun Ng, Bing Feng Dubey, Swapnil Henze, Gregor P. Chen, Wanyu Baskaran, Krishnamoorthy |
author_sort |
Yang, Shiyu |
title |
Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
title_short |
Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
title_full |
Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
title_fullStr |
Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
title_full_unstemmed |
Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
title_sort |
model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/160300 |
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1739837459852689408 |