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Making Homes Efficient and Comfortable Using AI and IoT Data

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Quby is a leading company offering data driven home services technology across European markets, known for creating the in-home display and smart thermostat Toon. We enable our partners to take on a leading role in the home services domain, by offering data driven home services. Our services enable 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, backed by Apache Spark, to generate actionable insights for all our end users. Via our IoT devices we have access to Europe’s largest energy dataset, petabytes in scale and growing exponentially. This unique dataset enables us to introduce new data driven services, with a particular focus on homes with smart meter installations.

In this talk, Ellissa will describe how machine learning is implemented on the Quby platform and will show multiple use cases backed by high-resolution IoT data. We’ll take a look at super resolution techniques for time series data, where using detailed high-resolution energy data is used to show personalized energy insights for users where only limited low-resolution energy data is available. We’ll show how ML algorithms offer the possibility for non-intrusive monitoring of elderly patients.

Ellissa will share the experiences from the Data Science and Data Engineering teams at Quby with bringing these data science algorithms from R&D to production using Databricks and the lessons learned in offering these services to hundreds of thousands of users on a daily basis.

Published in: Data & Analytics
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Making Homes Efficient and Comfortable Using AI and IoT Data

  1. 1. WIFI SSID: Spark+AISummit | Password: UnifiedDataAnalytics
  2. 2. Ellissa Verseput, Quby Making Homes Efficient and Comfortable Using AI and IoT Data #UnifiedDataAnalytics #SparkAISummit
  3. 3. Outline • Why, What, How Quby • 2 Example Use Cases – Bill Breakdown (Efficient) – Thermostat Program Advice (Comfortable) 3
  4. 4. 4 We believe the future can be better. Easier, more comfortable, and more sustainable. We help businesses and their customers to make this change without compromising on the important things in life.
  5. 5. 5
  6. 6. Z-Wave Meter adaptors Boiler adapters Gas sensor Philips Hue Z-Wave Central heating system Solar panels and power storage Smart plugs & smoke detectors Electricity sensor Gas Water Water sensor Electricity Athom Homey Amazon Alexa Google HomeDrebbleOlisto
  7. 7. 7#UnifiedDataAnalytics #SparkAISummit Over 400,000 connected homes across Europe Our partners:
  8. 8. Outline ü Why, What, How Quby • 2 Example Use Cases – Bill Breakdown (Efficient) – Thermostat Program Advice (Comfortable) 8
  9. 9. Energy insights gas & electricity (high frequency) Waste checker (~7 use cases) Water insights & saving tips Solar generation integration Smart thermostat functionality Smart home integration Air quality measurement & insights Monitoring the home Smart security solution Assisted living Energy insights with low frequency Dedicated app development for utilities with superior user experience & personal relevance Home services Efficient home Comfortable home Trusted home Current Quby Portfolio 9
  10. 10. Use Case #1 Bill Breakdown 10 Show how different appliances and activities in the home contribute to the energy bills
  11. 11. Key Technology: Load Disaggregation 11
  12. 12. Quby’s disaggregation algorithms 12 Patented algorithms can detect appliances from 10 second resolution electricity meter data
  13. 13. Bill Breakdown Categories for 10sec-data 13 Elec data Elec bill
  14. 14. 14 10sec-data VS 15min-data High Resolution Low Resolution
  15. 15. Bill Breakdown Categories for 15min-data 15 Elec bill Elec data
  16. 16. Super Resolution 16
  17. 17. 17 Utilizing our large database of high-resolution data, we apply advanced techniques to offer a more personalized, more dynamic and more accurate bill breakdown service. Low resolution data Bill breakdownEnhancement Most similar users with high resolution data Appliance detections High resolution data User Similar users
  18. 18. Bill Breakdown Architecture 18 Toon data collector P4 data collector API
  19. 19. Bill Breakdown for low resolution data 19
  20. 20. Outline ü Why, What, How Quby Ø 2 Example Use Cases • Bill Breakdown (Efficient) • Thermostat Program Advice (Comfortable) 20
  21. 21. Outline ü Why, What, How Quby Ø 2 Example Use Cases 🤯 Bill Breakdown (Efficient) • Thermostat Program Advice (Comfortable) 21
  22. 22. Use Case #2 Thermostat Program Advice 22 Suggest updates to the Toon users’ thermostat program, such that the program better reflects their behavioural patterns
  23. 23. Thermostat Program Advice Key Technology 23 Cooling down and warming up rate Humidity sensor Somebody home? Presence Detection
  24. 24. Non-intrusive Monitoring 24 Proof of Life – Toon can detect when a person is present Safety – Toon can indicate an active/inactive (elderly) resident Heating/Lighting – Toon can detect people are active to optimize heating
  25. 25. Training a Machine Learning Model 25 Hour Mon Tues Wed 08:00 Home Away Home 09:00 Away Away Home 10:00 Away Away Home Hour Mon Tues Wed 08:00 Home Home Home 09:00 Home Away Away 10:00 Away Away Away Cooling down and warming up rate Humidity sensor
  26. 26. We want to track model improvements and reproduce models Challenge
  27. 27. Solution: 27
  28. 28. This is how we did it 28
  29. 29. • We have saved the best, trained machine learning model in Python, • but for our production data pipeline we want to use Spark Scala Challenge
  30. 30. Pandas UDF Solution: Pandas UDF 30 Saved Model
  31. 31. This is how we did it 31
  32. 32. Thermostat Program Advice Architecture 32 Toon data collector Presence Labels API Survey Program Advice
  33. 33. What’s next? 33 8:00 Good to see that mom has woken up. I was worried since she complained she had breathing issues. 16:00 Guess the first thing he turned on was the game console. At least, I know he is back home from school 12:00 I left for work early morning, there shouldn’t be any recent activity. Perhaps I should have the neighbors check
  34. 34. Assisted Living 34 8:00 Good to see that mom has woken up. I was worried since she complained she had breathing issues. 16:00 Guess the first thing he turned on was the game console. At least, I know he is back home from school 12:00 I left for work early morning, there shouldn’t be any recent activity. Perhaps I should have the neighbors check We can monitor the home to help people care for their loved ones and keep their homes safe & warm.
  35. 35. Summary ü Why, What, How Quby ü 2 Example Use Cases ü Bill Breakdown (Efficient) ü Thermostat Program Advice (Comfortable) 35
  36. 36. This is Quby! careers@quby.com linkedin.com/company/quby/ Get in touch
  37. 37. Ellissa Verseput Machine Learning Engineer Ellissa.verseput@quby.com (+31) 6 133 688 21
  38. 38. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

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