移动感知科研项目完成-优雅草首次科研实验项目完成项目背景Background
The conventional building industry is challenged by strict demands
of energy efficiency and the increasing complexity of the built envi
ronment [1]. Smart buildings with the capability to dynamically
respond to climate, grids and users have been promoted in the past few
decades [2,3]. Intelligent monitoring and analytics of various types of
building performance data are fundamental prerequisites to achieve the
goals of smart building [4]. Among all monitoring performances, indoor
environmental quality (IEQ) is among the most important aspects
because people spend about 90% time indoors, and IEQ directly in
fluences human satisfaction, health and well-being [5–7]. Real-time IEQ
monitoring helps to detect poor or unexpected conditions [8,9] and
provides signals for the control of facilities, such as heating, ventilation
and air-conditioning (HVAC) systems, to enhance IEQ performance and
energy efficiency [10–12]. IEQ monitoring has thus become a major
focus of many building rating systems such as BREEAM [13], LEED [14]
and WELL [15] as well as measurement guidelines such as AHSRAE/
CIBSE/USGBC Performance Measurement Protocols for commercial
buildings [16].
Despite the importance of IEQ monitoring, its applications in real-life
buildings are often inadequate for fully grasping IEQ information, which
limits its value and effects on IEQ improvement and energy saving. The
key challenge is the complex spatio-temporal distribution characteristics
of IEQ due to the joint influences of outdoor climate, building envelopes,
space layout, human activity, equipment and other indoor disturbances
[17–20]. For example, Pollard et al. [17] found that the mean air tem
perature at different locations in a 1220 m2 space varied by >3 ◦C at
midday on workdays (spatial distribution), while Lee et al. [20] revealed
that the temporal variation of CO2 concentration could also have a wide
range of approximately 400 ppm (parts per million; 1 ppm equals 1 ml/
m3 ) between 9 a.m. and 6 p.m. on the same day (temporal distribution).
The temporal issue in IEQ monitoring can be solved by current
technology. Recent developments in the Internet of Things (IoT) and
Wireless Sensor Networks (WSNs) [21,22] have enabled the emergence
of a series of integrated, low-cost and intelligent IEQ monitoring systems
参考资料：
项目目标完成taskA 和 task B
项目取得的研究和结果以下是最终结果上一步的结果：
最终结果已被交付。
最终结果由于本实验的最终实验结果有保密性，因此成果暂不对外公开，整个科研课题完成后将对外部会有发布，敬请期待，目前首次完成实验已经开始下一步。
