The document discusses making a smart thermostat using various technologies like Arduino, Raspberry Pi, XBee for wireless communication, AWS for cloud storage and analytics using HBase and Spark. It describes collecting sensor data from the thermostat including 1000 temperature values per second and storing it in HBase on AWS cloud. It then discusses using machine learning algorithms like k-means clustering to analyze past temperature patterns and scenarios to optimize the thermostat's performance over time.

1. Making your own smart ‘machine learning’ thermostat
Data Donderdag – 19 november 2015 – Niek Temme

2. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
Quirky needs your Wink Hub
back after a botched security
update
- CNET -
18 – 50 billion
connected devices by 2020
- Gartner, IDC, Cisco -
device hub cloud

3. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
Quirky needs your Wink
Hub back after a botched
security update
- CNET -
18 – 50 billion
connected devices by
2020
- Gartner, IDC, Cisco -
device hub cloud
Device Connection
Cloud
- Storage
- Analitics
How Smart, Connected Products
Are Transforming Companies
Michael E. Porter James E. Heppelmann
oktober 2015

4. The device

5. 2
3
4
1

6. 2
3
4
1 A01_1933
A01_1933
A01_1982
A01_1982
A01_1933
A01_1884
A01_1933
A01_1982
A01_1933
40b5af00_rx000A01_8574094222_9670832 column=fd:cd, timestamp=1425905812807, value=1933
40b5af00_rx000A01_8574094222_9796804 column=fd:cd, timestamp=1425905812807, value=1933
40b5af00_rx000A01_8574094222_9919598 column=fd:cd, timestamp=1425905812807, value=1982
40b5af00_rx000A01_8574094223_9043289 column=fd:cd, timestamp=1425905812807, value=1982
40b5af00_rx000A01_8574094223_9166720 column=fd:cd, timestamp=1425905812807, value=1933
40b5af00_rx000A01_8574094223_9294077 column=fd:cd, timestamp=1425905812807, value=1884
40b5af00_rx000A01_8574094223_9416375 column=fd:cd, timestamp=1425905812807, value=1933
40b5af00_rx000A01_8574094223_9540631 column=fd:cd, timestamp=1425905812807, value=1982
40b5af00_rx000A01_8574094223_9669088 column=fd:cd, timestamp=1425905812807, value=1933

7. Connection & Storage
3 sensors
1000 values
per scecond
In one year
31 bilion rows
+/- 800GB

8. ?
Analytics

9. increase maintain
61 2 3 4 5 1 2 3 4 5 6
Smart Temp - Sensor value - 2015-03-19

10. 8
10
12
14
16
18
20
0 10 20 30 40 50 60
Temperature °C
Minutes
Act temp
Set temp
Making it smart
00:00 00:10 00:3000:20 00:40 00:50 01:00
6 min 5 min 4 min 3 min 2 min 2 min

11. Making it smart

12. Score
score =
( [number of times boiler goes on] * 10 * 60 ) +
( [total minutes boiler is on] * 2 ) +
( absolute( [maximum reached temperature]
– [set temperature] ) ) +
( if([overshoot]=true ) then 3600 else 0 )

13. 00:00 00:10 00:3000:20 00:40 00:50 01:00
4 min 4 min 4 min 3 min 3 min 2 min
00:00 00:10 00:3000:20 00:40 00:50 01:00
2 min 2 min 2 minoff off off
scenario
tempdif: 7 ℃
outempdif: 8 ℃
initial
improved
k-means clustering

14. Thank you
questions or feedback?
http://niektemme.com/2015/08/09/smart-thermostat/
niektemme@gmail.com
@niektemme
linkedin.com/in/niektemme

15. EXTRA SLIDES

26. • 1000 values per second
• Why 1000?
• Solved with in-memory database
Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
2 3 4
5 6 7

29. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
Quirky needs your Wink
Hub back after a botched
security update
- CNET -
18 – 50 billion
connected devices by
2020
- Gartner, IDC, Cisco -

30. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
2 3 4
5 6 7
I II III
Making your own smart ‘machine learning’ thermostat using
Arduino, AWS, HBase, Spark, Raspberry PI and XBee
niektemme.com/2015/08/09/smart-thermostat

31. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
2 3 4
5 6 7
I II III

32. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
2 3 4
5 6 7
I II III

33. Start with ‘IoT’

34. Used scenario
• Scenario = pattern of six 10 minute intervals
For example 5 4 3 2 2 2
• The tempdif = (set – inside) temperature
• The outtempdif = (set – outside) temperature
• Raspberry PI selects (given a tempdif and outtempdiff)
– 50% of the time: best temperature scenario
– 50% of the time: alternative scenario
• Used scenario and tempdif and outtempdiff used to select this
scenario are uploaded to the server.

35. Making it smart
Minutes per run – maintain room temperature Minutes per run – maintain room temperature (alternaKve)

36. Two step process
A. Score each used scenario
B. Cluster and rank scenarios in each
cluster

37. B. Cluster and select

38. Making it smart
8
10
12
14
16
18
20
0 10 20 30 40 50 60
Temperature °C
Minutes
Act temp
Set temp
8
10
12
14
16
18
20
0 10 20 30 40 50 60
Temperature °C
Minutes
Act temp
Set temp

39. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
2 3 4
5 6 7
I II III
00:00 00:10 00:3000:20 00:40 00:50 01:00
6 min 5 min 4 min 3 min 2 min 2 min

41. 61 2 3 4 5 1 2 3 4 5 6
increase maintain

44. 8
10
12
14
16
18
20
0 10 20 30 40 50 60
Temperature °C
Minutes
Act temp
Set temp
Making it smart
00:00 00:10 00:3000:20 00:40 00:50 01:00
6 min 6 min 6 min 2 min off 2 min

45. Arduino
+
XBee
RaspPI
+
XBee
AWS
HBase
Spark
Boiler
Device Connection
Cloud
- Storage
- Analitics
How Smart, Connected Product
Are Transforming Companies
Michael E. Porter James E. Heppelmann
oktober 2015

46. Personal journey