Quby is the creator and provider of Toon, a leading European smart home platform. We enable Toon users to control and monitor their homes using both an in-home display and app. As a data driven company, we use AI and machine learning to generate actionable insights for our end users. Using the data we collect via our IoT devices we have introduced multiple data driven services, including an energy waste checker and a boiler monitoring service. In this talk, Stephen will describe how AI and machine learning are implemented on the Toon platform, and will show multiple AI use cases relating to the connected home. We’ll take a look at how Deep Learning algorithms are used to detect inefficient appliances from electricity meter data and how streaming algorithms allow users to be alerted to anomalies with their heating systems in near real-time. Stephen will share the experiences from the Data Science and Data Engineering teams at Quby with bringing data science algorithms from R&D to production and the lessons learned in offering multiple data driven services to hundreds of thousands of users on a daily basis.

1. Stephen Galsworthy, Quby
AI and Machine Learning
for the Connected Home
Session hashtag: #SAISAI4

3. 2004
Home Automation
Europe is founded
2012
Home Automation
Europe partners
with Eneco to create
Toon
2013
Home Automation Europe
becomes Quby
Our history in milestones
2018
launch security service
ThuisWacht with Interpolis
2018
Launch Toon2
2006
Home control
alternatives are
prototyped

4. 4
Launched in 2016
Launched in 2017
Launched in 2012
Testing phaseOur partners:

5. SMART THERMOSTAT &
APP
TOON SOLAR BOILER MONITORINGWASTE CHECKER
MONTHLY ENERGY
INSIGHT
WATER INSIGHT
SECURITY
PACKAGE & APP
SMART METER
DONGLE & APP
ENERGY
INSIGHTS APP
DATA SERVICES

6. Z-Wave
Meter adaptor
Boiler adapter
Gas sensor
Philips Hue
Z-Wave
Central heating system
Solar panels
Click data
Smart plugs & smoke detectors
Electricity sensor

7. 100 MB data
per user
collected per
month
Over 350,000 displays
installed
300 types of
sensor and
user
interaction
data

8. For production grade
algorithms and R&D
For machine learning on
distributed data
- Spark cluster
management
- Collaboration using
shared notebooks
- Scheduling complex
workflows
- Dashboards
API management
Big data storage and
processing
Our Data Science platform

9. Creating data driven services

10. Feasibility
ViabilityDesirability

11. Traditional billing
relationship
Historical energy
insight
Real time
information
Appliance level
diagnostics
<4% 4-6% 8-10% 20%+
Maximizing energy savings and customer engagement
1Armel et. al., Stanford University (2011)
Increasingengagementbetween
customerandutility
Increasing energy savings potential1

12. Energy Waste Checker
“We don’t always notice how much energy we’re
wasting. Toon can now expose the energy
guzzlers in your home.”
Launched in December 2017 to all
Eneco Toon users

13. Toon detects inefficient appliances and behaviours
& more coming
soon

14. Electricity data Appliance signals
Key technology: load disaggregation algorithms

15. Quby’s disaggregation algorithms
DishwasherWashing
machine
Washing
machine(only night hours shown)
DryerDryer
Patent pending algorithms can detect appliances from 10 second resolution electricity meter data

16. Quby’s disaggregation algorithms
Washing
machine
Dryer DishwasherDryerWashing
machine
Washing
machine
Dryer Dishwasher Dryer
But users try to make it complicated for us…

17. Use case example: Inefficient dishwasher diagnosis
Disaggregation algorithms run on the 10s
electricity meter data
Compared with industry energy
consumption standards and peers
Translated to personalised
advice for the end user
Toon determines the “fingerprint” of
the appliance through features

18. Scale of the Waste checker
Each day we detect over
75,000dishwasher
cycles
That’s
13 years
of dishwashers running
continuously
and over
25% are
used
inefficiently

19. POP QUIZ
Which is the most
popular day of the week
to do the laundry?
?

20. It’s Sunday!
5.0
3.7
3.1
3.5
3.3
3.6
4.9
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
with 5.0 million of the 27 million washing machine detections we’ve made in 2018 so far

21. It’s Sunday!
k
100k
200k
300k
400k
500k
600k
0 2 4 6 8 10121416182022 0 2 4 6 8 10121416182022 0 2 4 6 8 10121416182022 0 2 4 6 8 10121416182022 0 2 4 6 8 10121416182022 0 2 4 6 8 10121416182022 0 2 4 6 8 10121416182022
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
with 5.0 million of the 27 million washing machine detections we’ve made in 2018 so far

22. Heating system anomaly detection
Enhancement to our existing Boiler
Monitoring service

23. 1. Boiler and thermostat data are constantly
monitored by Toon
4. Toon user is alerted
2. Toon determines when the
system leaves its normal operating
range
Heating system anomaly detection
3. Severity and user
preferences taken
into account

24. Heating system anomaly detection

25. 5 things we learned

26. #1: We love Spark
Machine learning on distributed data is key to all of this working at scale.
An example: Spark’s window functions work great for time series data.

27. #2: We prefer Scala now
Moving from Python to Scala takes effort but it’s worth it.
An example: We define our dataset schemas statically. Static typing and type
inference in Scala helps enforce these schemas.

28. #3: We live in the cloud
Rather than taking care of the infrastructure, data engineers can concentrate on how to
make data available to the business.
The Databricks platform gives us cluster timeouts, autoscaling, spot instances… ….all
without the need for additional FTEs.

29. #4: ‘In production’ is not the end
Continuous feedback from hundreds of thousands of users means there’s always
improvements to make.
An example: Our users told us that they wanted more information about their efficient
appliances, such as heat pump dryers. We productionized a deep learning model to give
them this info.

30. #5: Full-stack data science rocks!
Build and run, from initial concept to end result. Making services that our end users
will love.
Working together we deliver state-of-the-art algorithms operating at scale in
production.

31. Stephen Galsworthy
Head of Data Science
+31 (0) 20 462 1680
stephen.galsworthy@quby.com