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AI and Machine Learning for the Connected Home with Stephen Galsworthy

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AI and Machine Learning for the Connected Home with Stephen Galsworthy

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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.

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.

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AI and Machine Learning for the Connected Home with Stephen Galsworthy

  1. 1. Stephen Galsworthy, Quby AI and Machine Learning for the Connected Home Session hashtag: #SAISAI4
  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 MONITORINGWASTE 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. Creating data driven services
  9. 9. Feasibility ViabilityDesirability
  10. 10. 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
  11. 11. 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
  12. 12. Toon detects inefficient appliances and behaviours & more coming soon
  13. 13. Electricity data Appliance signals Key technology: load disaggregation algorithms
  14. 14. 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
  15. 15. Quby’s disaggregation algorithms Washing machine Dryer DishwasherDryerWashing machine Washing machine Dryer Dishwasher Dryer But users try to make it complicated for us…
  16. 16. 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
  17. 17. 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
  18. 18. POP QUIZ Which is the most popular day of the week to do the laundry? ?
  19. 19. 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
  20. 20. 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
  21. 21. Heating system anomaly detection Enhancement to our existing Boiler Monitoring service
  22. 22. 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
  23. 23. Heating system anomaly detection
  24. 24. 5 things we learned
  25. 25. #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.
  26. 26. #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.
  27. 27. #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.
  28. 28. #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.
  29. 29. #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.
  30. 30. Stephen Galsworthy Head of Data Science +31 (0) 20 462 1680 stephen.galsworthy@quby.com

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