Quby aims to reduce energy waste in European households by utilizing a data-driven platform that provides personalized advice to users on efficient appliance use. The company has developed a unified data analytics setup and artificial intelligence (AI) capabilities, resulting in significant energy savings, including the identification of 87 million inefficient appliance cycles and the conservation of 67 million kWh. Their approach includes continuous model training and deployment, enhancing collaboration and speeding up development cycles to support the transition to sustainable energy systems.

1. Saving Energy in Homes
with a Unified Approach to
Data and AI
Stephen Galsworthy, Chief Data & Product Strategy Officer, Quby
www.linkedin.com/in/galsworthy/
Erni Durdevic, Tech Lead – Data Science & Engineering, Quby
www.linkedin.com/in/ernidurdevic/

2. Our vision:
A world where living without
wasting natural resources is easy

3. Source: Eurostat
44%
Households use 44% of all natural gas
and 27% of all electricity in the EU
27%
But how much is just wasted?

4. We are not here to tell
people how much to
consume …
… but to make sure all their
use is intended.

5. 400,000 connected homes across Europe

6. Data Technology Stack: overview
IoT and Customer data Quby platform Personalised services
Cloud data storage AI control centre
• Advice & Insights
• Smart thermostat
control
• Home monitoring

7. Waste Checker
• We have found that over 40% of household appliances are inefficient or
being used inefficiently
“It looks like you often wash at high
temperatures. Your dishwasher needs
more energy to heat the water.
Washing at 50 degrees, or ECO mode, will
get your utensils just as clean, go ahead
and try it.”
Waste Checker offers personalised advice on
inefficient appliances and behaviours
Source: EU

8. Central heating
system
Water sensorElectricity sensorGas sensor
IoT Data Models Personalised
services
Terabytes of IoT data daily
Our data-driven services

9. Challenges
3. Over 1 million unsupervised
models trained daily
4. Struggling to track model
performance and
training/test datasets for a
wide range of algorithms
IoT Data Models Personalised
services
Patented algorithms
Our data-driven services
DishwasherWashing
machine
Washing
machine
DryerDryer
Patented algorithms

10. Personalised advice
on how to avoid
wasting energy
IoT Data Models Personalised
services
Our data-driven services

11. Batch processing
Our unified data analytics setup
IoT stream
Click data
Exploratory Data
Science Notebooks
Real time services
Live dashboards
SQL
Services API
SQL / NoSQL
AWS S3
Batch
Streaming
R & D
Model deployment
Model registry

12. Batch processing
Our unified data analytics setup
IoT stream
Click data
SQL
Services API
SQL / NoSQL
AWS S3
>1 TB per
day > 3 PB
Batch
>1 Million
unsupervised
models trained
daily

13. Batch processing
Unit tests
Electricity
Water
Presence
detection
Schedule
advice
…

14. Batch to Streaming
Batch
…
…
Streaming
• The same transformation will work for
Select
Filter
WithColumn
GroupBy
Join
…
UDF
Time-based window
Non time-based window functions (lead, lag,
…)

15. Batch processing
Our unified data analytics setup
IoT stream
Click data
Exploratory Data
Science Notebooks
SQL
Services API
SQL / NoSQL
AWS S3
https://github.com/quby-io/databricks-workflow

16. Demo – Who is it for?
Data Science teams that want to
get some IoT data pipelines in
production with Databricks.
https://github.com/quby-io/databricks-workflow

17. Demo – Why?
Help you to figure out a simple way to answer to the questions
• How do I set up a production pipeline in Databricks?
• How do I run unit tests on Spark transformations?
• How do I run integration tests on Notebook workflows?
• How do I rapidly prototype data transformations on real data?
• How do I manage multiple environments and configurations?
Why is it so special?
• Because it gives an insider view on a well-rounded way of working.
• It has multiple best practices pre-baked into a working example.
https://github.com/quby-io/databricks-workflow

18. Demo Databricks workflow
staging
raw
staging _features
electricity_power
dev_erni_features
electricity_power
power_features
initialize create_features
production
raw
production_features
electricity_powerinitialize create_features
integration_test
raw
integration_test_features
electricity_powerinitialize create_features
https://github.com/quby-io/databricks-workflow

19. Batch processing
Our unified data analytics setup
IoT stream
Click data
Exploratory Data
Science Notebooks
Real time services
Live dashboards
SQL
Services API
SQL / NoSQL
AWS S3
>1 TB per
day > 3 PB
>500 models
logged
Batch
Streaming
R & D
Model deployment
Model registry
>1 Million
unsupervised
models trained
daily
>15
deployments
tracked

20. Three crucial aspects can be simplified by applying
MLflow and model registry
1
Choosing the right model
2
Finding the latest model
3
Deploying the model to production

21. The source code of a model is not just the
code, but the combination of code,
configuration and data.

22. Choosing the right model before
?

23. Choosing the right model with MLflow

24. Finding the latest model before
?
”Hey Erni, where’s
the latest model?”

25. Finding the latest model with MLflow model registry

26. Model deployment before

27. Model deployment with MLflow model registry

28. Unified data analytics is powering Quby’s
transformation into a AI-first company
• Less time on spent on managing data infrastructure
• Collaboration and re-use of code and models across all
product teams
• Uniform way of working, including logging metrics and
tracking performance in MLflow
• Faster development cycles with new features delivered to
end users every 2 weeks

29. End result: Saving energy in homes across Europe
• Waste Checker, in the last 12 months:
• 87 million inefficient appliance cycles identified
• 67,000,000 kWh of wastage identified and targeted
• Thermostat Program Advice:
• Users automatically alerted when using gas whilst not at home
• 87,500 m3 of gas saved last winter
• Step-by-step we are enabling the transition to a sustainable energy system