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Quby - we create toon - Enabling smart energy services using scalable data science

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Toon, a leading European smart home platform, implements AI and machine learning to generate actionable insights for users. Stephen Galsworthy will describe the data driven services we’ve built which ensure that Toon users do not needlessly waste energy and can receive real-time alerts about problems with their heating systems.

Published in: Data & Analytics
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Quby - we create toon - Enabling smart energy services using scalable data science

  1. 1. 21 September 2018 Enabling smart energy services using scalable data science Stephen Galsworthy
  2. 2. 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
  3. 3. 4 Launched in 2016 Launched in 2017 Launched in 2012 Testing phaseOur partners:
  4. 4. SMART THERMOSTAT & APP TOON SOLAR BOILER MONITORING WASTE CHECKER MONTHLY ENERGY INSIGHT WATER INSIGHT SECURITY PACKAGE & APP SMART METER DONGLE & APP ENERGY INSIGHTS APP DATA SERVICES
  5. 5. 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
  6. 6. 100 MB data per user collected per month Over 350,000 displays installed 300 types of sensor and user interaction data
  7. 7. 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
  8. 8. Feasibility ViabilityDesirability Creating data driven services
  9. 9. 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) Increasingengagement betweencustomerandutility Increasing energy savings potential1
  10. 10. 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
  11. 11. Toon detects inefficient appliances and behaviours & more coming soon
  12. 12. Electricity data Appliance signals Key technology: load disaggregation algorithms
  13. 13. 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
  14. 14. Quby’s disaggregation algorithms Washing machine Dryer DishwasherDryerWashing machine Washing machine Dryer Dishwasher Dryer But users try to make it complicated for us…
  15. 15. 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
  16. 16. 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
  17. 17. POP QUIZ Which is the most popular day of the week to do the laundry? ?
  18. 18. It’s Sunday! 5.0 3.7 3.1 3.5 3.3 3.6 4.9 Sunday Monday Tuesday Wednesday Thursday Friday Saturday 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
  19. 19. POP QUIZ When is the most popular time to start the dishwasher? Day and hour ?
  20. 20. It’s Monday at 18:00! with 330k of the 20 million dishwasher detections we’ve made in 2018 so far k 50k 100k 150k 200k 250k 300k 350k 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
  21. 21. 5 things we learned
  22. 22. #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.
  23. 23. #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.
  24. 24. #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.
  25. 25. #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.
  26. 26. #5: Full-stack data scientists rock! 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.
  27. 27. Looking for a new challenge? Look no further! Quby – we create Toon, is looking to hire a DATA SCIENTIST YOU - are a problem-solving data genie - get excited about creating your own features and product - want to put all that theory into practice - love to visualize your data for storytelling - are a learning addict - are our future team member? Talk to me or e-mail our recruiter Tatjana at Careers@quby.com
  28. 28. With thanks to our data science partners: Stephen Galsworthy Head of Data Science +31 (0) 20 462 1680 stephen.galsworthy@quby.com
  29. 29. 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 Example: Heating system anomaly detection 3. Severity and user preferences taken into account

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