Quby aims to enhance home efficiency and comfort through AI and IoT data, illustrated by two use cases: bill breakdown for efficient energy management and thermostat program advice for user comfort. Their technology leverages advanced algorithms to provide personalized insights, monitor home activities, and offer predictive services. Quby operates over 400,000 connected homes across Europe, emphasizing a future that is easier, comfortable, and sustainable.

1. WIFI SSID: Spark+AISummit | Password: UnifiedDataAnalytics

2. Ellissa Verseput, Quby
Making Homes Efficient and Comfortable
Using AI and IoT Data
#UnifiedDataAnalytics #SparkAISummit

3. Outline
• Why, What, How Quby
• 2 Example Use Cases
– Bill Breakdown (Efficient)
– Thermostat Program Advice (Comfortable)
3

4. 4
We believe the future can be better.
Easier, more comfortable, and more sustainable.
We help businesses and their customers to make this change
without compromising on the important things in life.

5. 5

6. Z-Wave
Meter adaptors
Boiler
adapters
Gas sensor
Philips Hue
Z-Wave
Central heating
system
Solar panels and power
storage
Smart plugs & smoke detectors
Electricity sensor
Gas
Water
Water sensor
Electricity
Athom Homey Amazon Alexa Google HomeDrebbleOlisto

7. 7#UnifiedDataAnalytics #SparkAISummit
Over 400,000 connected homes across Europe
Our partners:

8. Outline
ü Why, What, How Quby
• 2 Example Use Cases
– Bill Breakdown (Efficient)
– Thermostat Program Advice (Comfortable)
8

9. Energy insights gas & electricity
(high frequency)
Waste checker (~7 use cases)
Water insights & saving tips
Solar generation integration
Smart thermostat functionality
Smart home integration
Air quality measurement & insights
Monitoring the home
Smart security solution
Assisted living
Energy insights with low frequency
Dedicated app development for utilities with superior user experience & personal relevance
Home services
Efficient home Comfortable home Trusted home
Current Quby Portfolio
9

10. Use Case #1
Bill Breakdown
10
Show how different appliances and
activities in the home contribute to the
energy bills

11. Key Technology: Load Disaggregation
11

12. Quby’s disaggregation algorithms
12
Patented algorithms can detect appliances from 10 second
resolution electricity meter data

13. Bill Breakdown Categories for 10sec-data
13
Elec data
Elec bill

14. 14
10sec-data VS 15min-data
High
Resolution
Low
Resolution

15. Bill Breakdown Categories for 15min-data
15
Elec bill
Elec data

16. Super Resolution
16

17. 17
Utilizing our large database of high-resolution data, we apply advanced techniques to offer a more
personalized, more dynamic and more accurate bill breakdown service.
Low resolution data
Bill breakdownEnhancement
Most similar
users with high
resolution data
Appliance
detections
High resolution data
User
Similar users

18. Bill Breakdown Architecture
18
Toon data
collector
P4 data
collector
API

19. Bill Breakdown for low resolution data
19

20. Outline
ü Why, What, How Quby
Ø 2 Example Use Cases
• Bill Breakdown (Efficient)
• Thermostat Program Advice (Comfortable)
20

21. Outline
ü Why, What, How Quby
Ø 2 Example Use Cases
🤯 Bill Breakdown (Efficient)
• Thermostat Program Advice (Comfortable)
21

22. Use Case #2
Thermostat Program Advice
22
Suggest updates to the Toon users’
thermostat program, such that the program
better reflects their behavioural patterns

23. Thermostat Program Advice Key Technology
23
Cooling down and
warming up rate
Humidity sensor
Somebody home?
Presence Detection

24. Non-intrusive Monitoring
24
Proof of Life – Toon can detect when a person is present
Safety – Toon can indicate an active/inactive (elderly) resident
Heating/Lighting – Toon can detect people are active to optimize heating

25. Training a Machine Learning Model
25
Hour Mon Tues Wed
08:00 Home Away Home
09:00 Away Away Home
10:00 Away Away Home
Hour Mon Tues Wed
08:00 Home Home Home
09:00 Home Away Away
10:00 Away Away Away
Cooling down and
warming up rate
Humidity sensor

26. We want to track model improvements and reproduce models
Challenge

27. Solution:
27

28. This is how we did it
28

29. • We have saved the best, trained machine learning model in Python,
• but for our production data pipeline we want to use Spark Scala
Challenge

30. Pandas
UDF
Solution: Pandas UDF
30
Saved
Model

31. This is how we did it
31

32. Thermostat Program Advice Architecture
32
Toon data
collector
Presence
Labels
API
Survey
Program Advice

33. What’s next?
33
8:00
Good to see that mom has
woken up. I was worried
since she complained she
had breathing issues.
16:00
Guess the first thing he
turned on was the game
console. At least, I know
he is back home from
school
12:00
I left for work early
morning, there
shouldn’t be any
recent activity.
Perhaps I should have
the neighbors check

34. Assisted Living
34
8:00
Good to see that mom has
woken up. I was worried
since she complained she
had breathing issues.
16:00
Guess the first thing he
turned on was the game
console. At least, I know
he is back home from
school
12:00
I left for work early
morning, there
shouldn’t be any
recent activity.
Perhaps I should have
the neighbors check
We can monitor the home to help
people care for their loved ones and
keep their homes safe & warm.

35. Summary
ü Why, What, How Quby
ü 2 Example Use Cases
ü Bill Breakdown (Efficient)
ü Thermostat Program Advice (Comfortable)
35

36. This is Quby!
careers@quby.com
linkedin.com/company/quby/
Get in touch

37. Ellissa Verseput
Machine Learning Engineer
Ellissa.verseput@quby.com
(+31) 6 133 688 21

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