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Forecasting 'What-if' Scenarios in Retail Using ML-Powered Interactive Tools

Forecasting 'What-if' Scenarios in Retail Using ML-Powered Interactive Tools

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<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>

<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>

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Forecasting 'What-if' Scenarios in Retail Using ML-Powered Interactive Tools

  1. 1. Saving Energy in Homes with a Unified Approach to Data and AI @Dr_Galsworthy @QubyEnergy
  2. 2. We believe that the future can be better. Easier, more comfortable, and more sustainable without compromising on the important things in life.
  3. 3. Worldwide annual energy consumption 109,136,000,000,000 kWh Source: IEA
  4. 4. 109,136,000,000,000 kWh Worldwide annual energy consumption Source: IEA But how much is just wasted?
  5. 5. EU energy saving target for 2030 -32.5% Source: EU
  6. 6. Source: Eurostat 44% Households use 44% of all natural gas and 27% of all electricity in the EU 27%
  7. 7. 400,000 connected homes across Europe
  8. 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. 9. IoT Data Models Outcomes Our data-driven services
  10. 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. 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. 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. 13. Our unified data analytics setup
  14. 14. Ingestion of data from multiple sources Our unified data analytics setup
  15. 15. Batch and streaming on the same data Our unified data analytics setup
  16. 16. Scalable pipelines to support reliable production services Our unified data analytics setup
  17. 17. Central place to develop new algorithms and use cases, quickly and cost effectively Our unified data analytics setup
  18. 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. 19. Explore a machine learning engineer’s perspective Wednesday
  20. 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

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