Indoor Air Quality Monitoring System for Smart Buildings Yu Zheng Microsoft Research.

Indoor Air Quality Monitoring System for Smart Buildings

Yu ZhengMicrosoft Research

Outdoor Air QualityAir quality monitor station

S1

S2

S4

S5

S8

S3

S6S7

S6

S9S10

S12

S11

S13 S14

S22S15

S16

S16

S17

S18

S19

S20

S21

50kmx40km

Indoor Air Quality• PM2.5 has NOT been monitored and dealt with• PM2.5 is NOT considered as a factor by HVAC

Problems!

HVAC: Heating, Ventilation, and Air Conditioning

PM2.5: Particulate matter with a diameter < 2.5

What We Do

• Monitor indoor PM2.5 and PM10– In 5 Microsoft Campuses in China– Deploy sensors on different floor of a building

What We Do

• Cloud + Client system

Indoor Air Quality

Data Receiver

Web Service

Cloud

Mobile Apps WebsitesSensors

5 : 0 2 5 : 0 5 5 : 0 8 5 : 1 1 5 : 1 4 5 : 1 7 5 : 2 00

2 5 0 05 0 0 07 5 0 0

1 0 0 0 0

Cou

nt

T i m e

P M 2 . 5

Client• Check air quality anytime• Inform people’s decision making

Urban Air

45

Suggestion to HVAC

• Purification Time (PT)

5:00 5:20 5:40 6:00 6:20 --20

30

40

50

60

70

80

t1

t0

Ind

oo

r P

M2

.5 c

on

ce

ntr

ati

on

(g

/m3 )

Scenario 1 Scenario 2

Time

t2

𝑃𝑇=𝑡1 −𝑡 0

8am7am

0.5h

1.5h

6:30am

• Depends on many factors– Outdoor/indoor air

quality– Meteorological data

80~120min40~80min

0~40min

Indoor/Outdoor

Temperature

Humidity

Pressure

Wind Speed

Suggestion to HVAC

• Learn the purification time from historical data– hourly outdoor air quality data– Hourly meteorological data

Indoor Air Quality

Data Receiver

Web Service

Cloud

Mobile Apps WebsitesSensors

Web Crawler Outdoor

Air quality MeteorologyKnowledge

Mining HVAC

Filters

Suggestion to HVAC

• Predict the purification time based on ANN

PTI

Outdoor AQI

Indoor AQI

Temperature

Humidity

Pressure

Wind Speed

w11

wij

w'11

w'jk

b1

b16

b'1

b'12

C

Suggestion to HVAC

• Replace HVAC’s filter sheet• Gap between the inferred and real PTs

1/17/2014 2/12/2014 2/21/2014 3/6/20140

20

40

60

80

Tim

e o

f p

uri

fic

ati

on

(m

inu

tes

)

Date

Real time PTI

Evaluation

• Data– A real dataset of 150 workdays from 12/23/2013 to 5/9/2014

generated in Beijing campus• Indoor air quality (every 10 minutes)• Hourly outdoor air quality from a monitoring station• Hourly meteorological data

• Baseline – Default: Historical longest time– Average– Linear Regression– ANN: without considering the meteorological data

Data is publicly available

Evaluation

• Results

0.0

0.2

0.4

0.6

0.8

1.0

0.81

1.00

0.55

0.72

Acc

ura

cy

0.87

0.0

0.5

1.0

1.5

2.02.00

0.33 0.33

0.14

Pu

rifi

cati

on

tim

e (H

ou

rs)

PTI ANN Regression Average Deault

0.15

Conclusion

• Deployed a real system in 5 MS campuses• Cloud + Client system– Inform people’s decision making– Suggestion to the operation of HVAC systems

Data, models and mobile client are publicly available ! Yu Zheng

Microsoft Researchyuzheng@microsoft.comUrban Air