暖通空调
自然通风
能源消耗
热舒适性
通风(建筑)
模拟
能量回收通风
控制(管理)
能量(信号处理)
计算机科学
地铁列车时刻表
汽车工程
控制系统
工程类
建筑工程
空调
机械工程
人工智能
电气工程
物理
操作系统
统计
热力学
数学
作者
Yi Chen,Zheming Tong,Holly Samuelson,Wentao Wu,Ali Malkawi
标识
DOI:10.1016/j.egypro.2019.01.1004
摘要
As an increasingly popular green building technology, natural ventilation (NV) is an effective solution for better thermal comfort and lower HVAC system energy consumption. However, to achieve NV’s full potential in practice, it is critical to control windows and HVAC systems. Three main types of control schemes are examined in this study: spontaneous occupant control, informed occupant control, and fully automatic control. Five representative climates, ranging from hot, temperate, to severely cold, are tested for the effectiveness of each control scheme. The results confirmed the superior performance of the fully automatic system, especially with the model predictive control algorithm, which demonstrates a cooling energy saving of 17%–80%, with zero discomfort degree hours. Neither the informed or spontaneous occupant controls are able to maintain the indoor temperature within the comfort range at all times. In particular, the informed occupant operation following the fixed-schedule four-times-daily signals shows the worst thermal control capacity and leads to 1500–4000 discomfort degree hours. In terms of energy performance, the informed occupant control, by following the heuristic control signals, shows the least energy savings and even indicates energy waste in some scenarios. Based on the study’s results, it is recommended to either adopt the fully automatic natural ventilation control system to achieve maximum energy-saving potential or allow occupant autonomy for natural ventilation controls to achieve a lower budget for initial installation and maintenance cost.
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