This study is a first large-scale analysis of the performance of a cloud connected and smart residential mechanical extract ventilation (MEV) system based on field data. About 350 units were analysed over a period of 4 months from December 2018 up to March 2019, corresponding with the main winter period in Belgium. Half of the units were installed as a smartzone system which means additional mechanical extraction from habitable rooms as bedrooms.
The air extraction was controlled on different parameters (humidity, CO2 and VOC) depending on the room type. Indoor climate and IAQ were analysed with respect to design criteria set out in standards as well as fan characteristics and energy consumptions.
Since the ventilation systems are controlled on humidity, periods of RH levels >80% were limited. The typical RH ranges (between 30 and 70% or 25 and 60%) set out in standards for habitable spaces were fulfilled during at least 80% of the time, without causing complaints from the users.
On average, the CO2 level in bedrooms was <950 ppm during at least 90% of the nighttime. When comparing the MEV with smartzone to literature data of a similar system, the exposure (in ppm.h) to CO2 >1200 ppm was reduced to 33%, while the ventilation heating energy consumption was 32% lower. This implies a ctrl-factor of 0.26 for the MEV system with smartzone, which is substantially lower than the default values currently used in regulation. The auxiliary energy consumption of the MEV with smartzone was found to be less than 50% of the literature values reported on similar systems.
The average total yearly energy cost related to the operation of the ventilation system (heating and auxiliary energy) was found to be limited to €100, and at least comparable to the operating cost of a MVHR system. Since rooms are often unoccupied or occupied at a low level, advanced demand control technology proves to have a high potential to limit total energy consumption, while assuring a good IAQ.
Large-scale performance analysis of a smart residential MEV system based on cloud data
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Year:
2019
Languages: English | Pages: 15 pp
Bibliographic info:
40th AIVC - 8th TightVent - 6th venticool Conference - Ghent, Belgium - 15-16 October 2019