<p>Energy wastage by residential buildings is a significant contributor to total worldwide energy consumption. Quby, an Amsterdam based technology company, offers solutions to empower homeowners to stay in control of their electricity, gas and water usage. Using Europe’s largest energy dataset, consisting of petabytes of IoT data, the company has developed AI powered products that are used by hundreds of thousands of users on a daily basis. In this talk Stephen will describe Quby’s approach, explore the challenges of scaling data products and explain the empowering role of unified analytics and the Databricks platform towards the data, the technology and the organization itself.</p>

1. Saving Energy in Homes with a
Unified Approach to Data and AI
@Dr_Galsworthy
@QubyEnergy

2. We believe that the future can be better.
Easier, more comfortable, and more sustainable
without compromising on the important things in life.

3. Worldwide annual energy consumption
109,136,000,000,000 kWh
Source: IEA

4. 109,136,000,000,000 kWh
Worldwide annual energy consumption
Source: IEA
But how much is just wasted?

5. EU energy saving target for 2030
-32.5%
Source: EU

6. Source: Eurostat
44%
Households use 44% of all natural gas
and 27% of all electricity in the EU
27%

7. 400,000 connected homes across Europe

8. Waste Checker
• Efficient appliances expected to save consumers €100 billion annually
by 2020
• 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

9. IoT Data Models Outcomes
Our data-driven services

10. Central heating
system
Water sensorElectricity sensorGas sensor
IoT Data Models Outcomes
Challenges
1. Over 3 petabytes of IoT data
and rapidly growing
2. Need to process IoT data,
both batch and streaming, in
a reliable and efficient
manner
Terabytes of IoT data daily
Our data-driven services

11. 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 Outcomes
DishwasherWashing
machine
Washing
machine
DryerDryer
Patented algorithms
Our data-driven services

12. Personalised advice
on how to avoid
wasting energy
Challenges
5. Reliable services needed for
hundreds of thousands of
daily users
IoT Data Models Outcomes
Our data-driven services

13. Our unified data analytics setup

14. Ingestion of data
from multiple
sources
Our unified data analytics setup

15. Batch and
streaming on
the same data
Our unified data analytics setup

16. Scalable pipelines to
support reliable
production services
Our unified data analytics setup

17. Central place to develop
new algorithms and use
cases, quickly and cost
effectively
Our unified data analytics setup

18. Unified data analytics is powering Quby’s
transformation into a AI-first company
• Collaboration by multiple groups on same platform
• Data scientists, data engineers, BI analysts, developers,
infra and customer support
• Data team adding more value: less time on infrastructure
• Typical data engineering to scientist ratio = 5:1
• Quby’s ratio DE:MLE:DS = 1:1:1
• Faster development cycles
• Our first data service > 12 months
• Quby now < 8 weeks

19. Explore a machine learning engineer’s perspective
Wednesday

20. Saving energy in homes across Europe
• In the last 12 months:
• 87 million inefficient appliance cycles identified
• 67,000,000 kWh of wastage identified and targeted
• A significant reduction in household energy bills when combined with our smart
thermostat to control heating
• Step-by-step we are enabling the transition to a sustainable energy system