4.7 Article

Experimental investigation on model predictive control of radiant floor cooling combined with underfloor ventilation system

期刊

ENERGY
卷 176, 期 -, 页码 23-33

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2019.03.102

关键词

Radiant floor cooling; Underfloor ventilation; Model predictive control; Proportion-integral-derivative control; Control performance

资金

  1. National Natural Science Foundation of China [51408302]
  2. Jiangsu Province Qing Lan Projects of China (2016)
  3. scientific research fund of Nanjing Institute of Technology [CKJA201803]

向作者/读者索取更多资源

Energy saving potential of radiant floor cooling combined with underfloor ventilation (RFCUV) system has been restricted by its control problems. Existing conventional control methods of radiant cooling system have several disadvantages, such as control lag, poor control performance, and low economy, etc. The objectives of this study were to: (i) build a dynamic simplified model and validate its precision experimentally; (ii) implement advanced model predictive control (MPC) on RFCUV system; and (iii) demonstrate MPC control performance by comparing with existing conventional proportional-integral-derivative (PID) experimentally. Experimental results indicated that under experimental step setpoint variations, the adjusting time of indoor air temperature or operative temperature was only 12 min with MPC controller, and was 30 min with PID controller; it took only 1 min to reach recommended thermal comfort range with MPC controller, and 17 min with PID controller. During 9:00 to 17:00 in typical design day of Nanjing city, compared with PID controller, MPC controller yielded 17.5% energy saving when maintaining equal or better indoor comfort. Thus, compared with PID, MPC demonstrated the advantages of rapid responses, good stability and excellent energy saving effect in RFCUV system. (C) 2019 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据