暖通空调
热舒适性
能源消耗
集合(抽象数据类型)
汽车工程
计算机科学
高效能源利用
能量(信号处理)
室内空气质量
模拟
建筑工程
空调
可靠性工程
工程类
机械工程
环境工程
数学
热力学
统计
电气工程
物理
程序设计语言
作者
Ali Ghahramani,Farrokh Jazizadeh,Burçin Becerik-Gerber
标识
DOI:10.1016/j.enbuild.2014.09.055
摘要
HVAC systems are responsible for providing acceptable thermal conditions and indoor air quality for building occupants. Increasing thermal comfort and reducing HVAC related energy consumption are often seen as conflicting goals. Few researchers have investigated the feasibility of reducing HVAC related energy consumption by integrating occupants’ personalized thermal comfort preferences into the HVAC control logic. In this study, we introduce a knowledge-based approach for improving HVAC system operations through coupling personalized thermal comfort preferences and energy consumption patterns. In our approach, thermal comfort preferences are learned online and then modeled as zone level personalized comfort profiles. Zone temperature set points are then selected through solving an optimization problem for energy, with comfort, indoor air quality, and system performance constraints taken into consideration. In the case that acceptable comfort levels for all occupants of a zone were not achievable, the approach selects set points that minimize the overall thermal discomfort level. Compared to an operational strategy focusing on comfort only, evaluation of our approach, which aims for both maintaining or improving comfort and reducing energy consumption, showed improvements by reducing average daily airflows for about 57.6 m3/h (12.08%) in three target zones.
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