globalchange  > 气候变化与战略
DOI: 10.1016/j.scs.2019.101908
论文题名:
Identification of the optimal control strategies for the energy-efficient ventilation under the model predictive control
作者: Fang J.; Ma R.; Deng Y.
刊名: Sustainable Cities and Society
ISSN: 22106707
出版年: 2020
卷: 53
语种: 英语
英文关键词: Cost function ; Efficient control strategy ; Model predictive control ; Thermal comfort
Scopus关键词: Air conditioning ; Air pollution ; Cost functions ; Energy efficiency ; Energy utilization ; HVAC ; Optimal control systems ; Predictive control systems ; Thermal comfort ; Ventilation ; Application scenario ; Efficient control ; Indoor thermal comfort ; Operational parameters ; Optimal control strategy ; Optimal cost function ; Predictive modeling ; Ventilation control ; Model predictive control
英文摘要: The significant energy use and resultant air pollutants emissions from the HVAC system pose grave concerns to the global society. The model predictive control (MPC) approach is found to be an effective and economic way to optimally regulate the operational parameters of the HVAC system. In this study, the authors focus on the control of the ventilation of the HVAC system under two objectives: minimal energy consumption and high degree of indoor thermal comfort. The authors present a first comprehensive study to investigate the influences of cost function formulations on MPC control of the overall performance, and manage to identify the optimal cost function design for the ventilation control. This study incorporates the non-linear power predictive model and PMV calculations into the cost function in addition to the traditional linearized power and PMV models. The results indicate that with non-linear power and PMV calculations, the MPC controller could perform much better in terms of both the energy consumption and indoor thermal comfort. By defining conversion efficiency as the ratio between PMV change and energy consumption decrease, the optimal control strategy, proposed by the authors, can lead to a range of 29.2% to 49.8% efficiency elevation depending on the application scenarios. © 2019 Elsevier Ltd
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159916
Appears in Collections:气候变化与战略

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作者单位: Department of Civil and Environmental Engineering, Soochow University, Suzhou, Jiangsu, China

Recommended Citation:
Fang J.,Ma R.,Deng Y.. Identification of the optimal control strategies for the energy-efficient ventilation under the model predictive control[J]. Sustainable Cities and Society,2020-01-01,53
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