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GrowSmarter Webinar : Generating Insights from Energy Household by AGT

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Presentation on generating insights from Energy Households by project partner AGT.

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GrowSmarter Webinar : Generating Insights from Energy Household by AGT

  1. 1. Generating Insights from Energy Household Data Stefaniia Legostaieva - Data Scientist at AGT Manuel Görtz – Project Manager at AGT 18th September 2018, Darmstadt Stefaniia Legostaieva - Data Scientist at AGT Manuel Görtz – Project Manager at AGT 18th September 2018, Darmstadt This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 646456. The sole responsibility for the content of this presentation lies with the GrowSmarter project and in no way reflects the views of the European Union.
  2. 2. AGT is a pioneer in IoT and Social Data management, Big Data integration and advanced analytics. MANUFACTURING & ENERGY SMART TRAFFIC MANAGEMENT SMART CITIES NEW MEDIA CONTENT SPORTS & ENTERTAINMENT EVENTS Private organization FOUNDED in 2007 Headquartered in SWITZERLAND >$8B in IoT projects R&D CENTERS in IL, DE, U.S. > 250 engineers ~$1B ANNUAL REVENUE Profitable, CFP
  3. 3. INTRODUCTION Project goal To reduce energy consumption in private sector Solution with the help of smart plugs and insights about energy consumption of individual devices AGT proprietary and confidential
  4. 4. AGENDA System Deployment with Fibaro Smart plugs and Homee Gateway in Smart Homes Dashboard Web-based application (Dashboard) for device-level energy awareness in Smart Homes Services / analytics Awareness Activity recognition and behaviour analytics Demo AGT proprietary and confidential
  5. 5. SYSTEM ARCHITECTURE
  6. 6. ENERGY AWARENESS DASHBOARD GrowSmarter Insight Dashboard Web-based application for device-level energy awareness in Smart Homes  Productized and deployed on AWS  Real-time data collection, processing and analytics  Scalable up to 10 000 households AGT proprietary and confidential
  7. 7. ANALYTICS / SERVICES Smart Home Energy Analytics Services Awareness  Device usages statistics  Connected device type recognition Activity recognition and behaviour analytics  Device usage mode recognition  Device abnormal behavior detection Recommendations  Device replacement AGT proprietary and confidential
  8. 8. ENERGY AWARENESS DASHBOARD Device energy consumption costs Value - how much and electrical energy do I spend on each home appliance? AGT proprietary and confidential
  9. 9. ENERGY AWARENESS DASHBOARD Device usages statistics Value - how many times do I use a device? - for how long do I use a device? - how much it costs (based on provided price per kWh)? AGT proprietary and confidential
  10. 10. Device Mode Recognition Value - how much the fast program of my dishwasher consumes comparing to the eco program, how much does it cost? Analytics Analyzing consumption behavior of a user  Comparison of different modes of usages within a device  Recommendations of using energy efficient mode based on consumption behavior ENERGY AWARENESS DASHBOARD RegularEcoFast Dishwasher AGT proprietary and confidential
  11. 11. Device Type Recognition Value - did I plug a new device, shall my devices be re-arrange based on a device type? Analytics  Detect when and what kind of new device was connected  Re-group devices in the dashboard based on the device and not the smart plug ENERGY AWARENESS DASHBOARD KettleMicrowave Washing machineTVAGT proprietary and confidential
  12. 12. Device Abnormal Behavior Recognition Value - does my device work normally? - do I use my device as usually? Analytics Analyzing consumption patterns of a device  Detect abnormal states of the device  Detect abnormal use of the device ENERGY AWARENESS DASHBOARD normalanomalous Abnormality: door is open for too long AGT proprietary and confidential
  13. 13. Data  Public dataset  Office data ANALYTICS - PIPELINE AGT proprietary and confidential  Semantic data type matching  Zero consumption removal  Data fusion Data cleaning Data preprocessing  Semantic data type matching  Zero consumption removal  Transforming into windows of pre- defined size: 1 hour, 30 or 15 minutes Feature extraction  Statistical features extraction: mean, max, min, variance, peaks, time to max  DTW (Dynamic time wrapping) as similarity measure Exploratory analysis Model building  Random Forest classifier  KNN (K-nearest neighbors) Evaluation Visualization  Confusion matrix  F1 measure  Plots Make decision Build data product Device type recognition ⁻ approach 1 ⁻ approach 2 feedback loop User’s feedback
  14. 14. DEMO
  15. 15. THANK YOU Stefaniia Legostaieva Data Scientist at AGT SLegostaieva@agtinternational.com

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