Dynamic optimal airflow ventilation can have a great impact on the indoor air distribution and pollutant removal to improve the indoor air quality while saving energy. An online monitoring and control ventilation system has been developed and evaluated using fast prediction models and micro-control. An environmental chamber (1.8 m3) was used for the evaluation to monitor the CO2 dispersion under different air change rates and air speed. Specifically, an artificial neural network model based on a low-dimensional linear ventilation model was introduced and validated to provide environmental control and rapid prediction of pollutant concentration distribution in the indoor environment, which can save computing time and significantly enhance energy saving efficiency up to 16–47%. The validation was carried out by comparison with measurement data of the chamber experiment. An induction system was applied to locate and monitor the personnel in the office due to pollution that are generated by people. A ZigBee wireless module would transmit location information of pollutant source (i.e. CO2 generated by occupants) and to determine the optimal ventilation mode based on ventilation assessment to achieve automatic control of indoor air quality to ensure the wellbeing of occupants while saving energy.
ZhangYHeCQTangBJWeiYM.China’s energy consumption in the building sector: a life cycle approach. Energy Build2015;
94: 240–251.
2.
WangXFengWCaiWRenHDingCZhouN.Do residential building energy efficiency standards reduce energy consumption in China? – A data-driven method to validate the actual performance of building energy efficiency standards. Energy Policy2019;
131: 82–98.
3.
LiXTYuC.China’s building energy efficiency targets: challenges or opportunities?Indoor Built Environ2012;
21: 609–613.
4.
ShipworthMUcciM.People and energy use in the indoor and built environment. Indoor Built Environ2015;
24: 863–866.
5.
WuY.China building energy efficiency: current status, issues, and policy recommendations. Beijing: China Ministry of Construction, 2003.
6.
HongTZ.A close look at the China Design Standard for Energy Efficiency of public buildings. Energy Build2009;
41: 426–435.
7.
KimJTYuC.Sustainable development and requirements for energy efficiency in buildings – the Korean perspectives. Indoor Built Environ2018;
27: 734–751.
8.
HanZYWengWGHuangQYFuMYangJLuoN.Aerodynamic characteristics of human movement behaviours in full-scale environment: comparison of limbs pendulum and body motion. Indoor Built Environ2019;
24: 87–100.
9.
ZhouLChenWY.Status and decomposition analysis on building energy consumption of China in the last decade. J Renew Sustain Energy2015;
7: 063134.
10.
OwenAMitchellG.Outside influence – some effects of retrofit installers and advisors on energy behaviours in households. Indoor Built Environ2019;
24: 925–936.
11.
MengXGaoYHouCYuanF.Questionnaire survey on the summer air-conditioning use behaviour of occupants in residences and office buildings of China. Indoor Built Environ2019;
28: 711–724.
12.
DengHYFengZCaoSJ.Influence of air change rates on indoor CO2 stratification in terms of Richardson number and vorticity. Build Environ2017;
129: 74–84.
13.
CostanzoSCusumanoAGiaconiaC.Ventilation rates and unsatisfied percentage from indoor CO2 concentration. Indoor Built Environ2011;
20: 232–245.
14.
FanYItoK.Integrated building energy computational fluid dynamics simulation for estimating the energy-saving effect of energy recovery ventilator with CO2 demand-controlled ventilation system in office space. Indoor Built Environ2014;
23: 785–803.
15.
Haverinen-ShaughnessyUMoschandreasDJShaughnessyRJ.Association between substandard classroom ventilation rates and students’ academic achievement. Indoor Air2011;
21: 121–131.
16.
DaiseyJMAngellWJApteMG.Indoor air quality, ventilation and health symptoms in schools: an analysis of existing information. Indoor Air2003;
13: 53–64.
17.
Bakó-BiróZClements-CroomeDJKochharNAwbiHBWilliamsMJ.Ventilation rates in schools. Build Environ2008;
43: 362–367.
18.
ColeyDABeisteinerA.Carbon dioxide levels and ventilation rates in schools. Int J Ventil2002;
1: 45–52.
19.
CaoSJ.Challenges of using CFD simulation for the design and online control of ventilation systems. Indoor Built Environ2019;
28: 3–6.
20.
WangHLuSChengJZhaiZJ.Inverse modeling of indoor instantaneous airborne contaminant source location with adjoint probability-based method under dynamic airflow field. Build Environ2017;
117: 178–190.
21.
ZhangTFYinSWangSG.An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source. Atmos Environ2013;
77: 62–77.
22.
DestaTZBrechtAVMeyersJBaelmansMBerckmansD.Combining CFD and data-based mechanistic (DBM) modelling approaches. Energy Build2004;
36: 535–542.
23.
MontgomeryJFStoreySBartlettK.Comparison of the indoor air quality in an office operating with natural or mechanical ventilation using short-term intensive pollutant monitoring. Indoor Built Environ2015;
24: 777–787.
24.
LeeSHwangboSKimJTYooCK.Gain scheduling based ventilation control with varying periodic indoor air quality (IAQ) dynamics for healthy IAQ and energy savings. Energy Build2017;
153: 275–286.
25.
VakiloroayaVSamaliBFakharAPishghadamdK.A review of different strategies for HVAC energy saving. Energy Convers Manage2014;
77: 738–754.
26.
ChengYLinZ.Experimental study of airflow characteristics of stratum ventilation in a multi-occupant room with comparison to mixing ventilation and displacement ventilation. Indoor Air2015;
25: 662–671.
27.
MaZSunS.Numerical assessment of the effect of cigarette smoking on indoor PM2.5 distribution and study of ventilation strategies. Indoor Built Environ2016;
27: 369–379.
28.
ZhangTChenQ.Novel air distribution systems for commercial aircraft cabins. Build Environ2007;
42: 1675–1684.
29.
ZhouXDLinghuJBXuJWTanLQiKLFuXX. Control method and device allowing electric fan to track human bodies. China Patent CN103925225, Beijing, 2014.
30.
ArensEAZhangHAnwarG. Interactive occupant-tracking fan for indoor comfort and energy conservation. U.S. Patent Application 15/178,260[P]. 2016.
31.
LiuSYinLHoWKLingKVSchiavonS.A tracking cooling fan using geofence and camera-based indoor localization. Build Environ2017;
114: 36–44.
32.
CaoSJRenC.Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network. Build Environ2018;
144: 316–333.
33.
LeiLWangSZhangT.Optimal specification of target temperature points for inverse design of an indoor thermal environment. Build Environ2015;
92: 518–527.
34.
ChenCLinCHLongZChenQ.Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method. Indoor Air2014;
24: 81–92.
35.
ChenXLiAGaoR.Numerical investigation on particle deposition in a chamber with an attached-wall heat source. Indoor Built Environ2014;
23: 640–652.
36.
HabchiCGhaliKGhaddarNChakrounWAlotaibiS.Ceiling personalized ventilation combined with desk fans for reduced direct and indirect cross-contamination and efficient use of office space. Energy Convers Manag2016;
111: 158–173.
37.
HomodRZSahariKSMAlmuribHAFNagiFH.RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD. Build Environ2012;
49: 141–153.
38.
DestaTZJanssensKBrechtAVMeyersJBaelmansMBerckmansD.CFD for model-based controller development. Build Environ2004;
39: 621–633.
39.
CaoSJMeyersJ.On the construction and use of linear low‐dimensional ventilation models. Indoor Air2012;
22: 427–441.
40.
RenJCaoSJ.Incorporating online monitoring data into fast prediction models towards the development of artificial intelligent ventilation systems. Sustain Cities Soc2019;
47: 101498.
41.
Ren C and Cao SJ. Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control. Sustain Cities Soc 2019; 51: 101673.
42.
Feng Z, Yu CW and Cao SJ. Fast prediction for indoor environment: models assessment. Indoor Built Environ 2019; 28: 727–730.
43.
CaoSJZhuDHYangYB.Associated relationship between ventilation rates and indoor air quality. Rsc Adv2016;
6: 111427–111435.
44.
WhelenTSiqueiraP.Coefficient of variation for use in crop area classification across multiple climates. Int J Appl Earth Observ Geoinform2018;
67: 114–122.
45.
CaoSJMeyersJ. Asymptotic conditions for the use of linear ventilation models in the presence of buoyancy forces. Build Simul2014; 7: 131–136.
