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A use case of data analysis for assessing Indoor Air Quality indicators

François-Yves Prévost, Xavier Boulanger, Christophe Barras, 2018
ventilation | IAQ connected object | simulation | air stuffiness indicator | PM2.5
Bibliographic info: 39th AIVC Conference "Smart Ventilation for Buildings", Antibes Juan-Les-Pins, France, 18-19 September 2018
Languages: English Pages (count): 12

Product connectivity makes products and systems remotely controllable and possibly interoperable with other devices in the house. 

The most common way to achieve this interoperability is to connect these devices locally. On the other hand, products may also be cloud-connected, which allows an easier and seamless interoperability between devices. Hence, data are collected and stored in the cloud. As soon as the measured data is sent to the cloud, large set of data are available and can be anonymously retrieved and statistically analyzed. 

Whereas until recently analysis were performed on a small number but well identified and sometimes perfectly customized test sites, we have now access to large scale real life data from ordinary people living in real houses. 

Thanks to the availability of miniaturized and affordable technology, we have developed and deployed low cost measurement systems embedding a limited but relevant number of sensors. Measured parameters are temperature and relative humidity, CO2, VOC and PM2.5. Data were recorded over a period of time in 2016 and from March to summer 2018. 

For the purpose of the study, we retrieved the data on a sample of customers’ houses equipped with dedicated indoor air quality (IAQ) connected objects and either individual exhaust or bidirectional ventilation system. 

A theoretical approach by simulation was first carried out on four ventilation strategies in a four room dwelling to present the indicators before testing them on field data. The constant airflow bidirectional ventilation system appeared to be the one ensuring the best IAQ in main rooms while humidity controlled exhaust ventilation appeared to be the one ensuring the best comfort in the bathroom. 

Assessing CO2 levels is relevant in periods where people are present. That is why an algorithm for the detection of people from CO2 concentration analysis was developed. Thus, it is possible to automatically calculate CO2 based IAQ indicators. We performed the calculation for four examples of IAQ field data and results were compared. The conclusion of this comparison is that ICONE is the best indicator between the three we analyzed: the scale is easy to understand, the output is independent of the length of the measurement period and it is less sensitive to threshold effect. 



The availability of these easily accessible data will help to raise the awareness of all stakeholders regarding the importance of verified and guaranteed results. 

The study demonstrates that this data allows a future refinement of models used for normative evaluation of systems. 

Another conclusion was obtained regarding PM 2.5 pollution. The IAQ object collects outdoor air quality information computed from publicly available data, and compares it to inside measurement. In an occupied dwelling, outdoor and indoor levels are strongly correlated, which means that PM2.5 indoor concentration is mainly driven by the outside concentration. The human activity inside has no or little influence on the level. 


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