Flutura presentation - The Hive india event
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1. The document discusses how machine-to-machine (M2M) technology and big data analytics are disrupting the energy industry by improving distribution networks through the use of smart meters. 2. Smart meters collect data on voltage, current, power usage, and outage events which can then be used to detect theft, map outages, forecast demand, and optimize field technician deployment to reduce losses in the distribution system. 3. Key applications discussed include using smart meter data to detect theft hotspots, implement dynamic electric pricing to encourage off-peak usage, and analyze equipment maintenance patterns to predict and prevent outages.
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Flutura presentation - The Hive india event
- 2. 2 Agenda – 3 Key Things 1. Deep dive & Explore Smart meter M2M Use Case 2. How M2M unlocks value in Oil N Gas, Building risk & Telecom ? 3. 3 key “gotchas” from the trenches
- 3. 3 What's common to Cows in Netherland & New Zealand ?
- 4. 4 `2008 : Sensors > Humans !Unnoticed inflection point ... Nose to tail sensor jungle Sensors Humans
- 5. 5 How M2M + Big Data Analytics is disrupting the Energy industry ?
- 6. 6 The Electricity Value chain Power generation Transmission substation High voltage electric transmission system Distribution Substation Voltage is reducedDistribution Transformer Household Generation + Transmission + Distribution
- 7. 7 Upto 40 % of Power transmitted does not reach end consumer Distribution considered the weak link Why is Distribution considered a weak link in the entire power value chain ? What's the Problem we are solving ?
- 8. 8 Pain-1 : Large Scale Theft !
- 9. 9 Which grid components need to be replaced based on age and outage frequencies ? Pain-2 : Ageing + inadequate distribution network
- 10. 10 How do we map street level outage360 behaviour ? proactive incident response Pain-3 : Frequent Outages
- 11. 11 Pain-4 : Grid energy spot pricing ... Load forecasting !
- 12. 12 Pain-5: Sub Optimal use of electricity by households Inefficient bulbs Switching from inefficient incandescent lights to more efficient light bulbs such as compact fluorescent lights (CFLs) ALTERING HOUSEHOLD BEHAVIOR KEY!
- 13. 13 • Where should Electricians to be deployed to the right spots in real time • Where should they be deployed to proactively prevent outages Pain-6: Optimising Field Electrician force!
- 14. 14 Why is it important to solve this problem? A 1 % reduction in AT&C unlocks millions of $
- 15. 15 How does a Smartmeter help ? • Smart meter Data collected • COV – Voltage – Current – Power – Harmonic voltage distortion • Events – Tamper events • Reverse energy flows – Outage events – Meter hardware events • Battery low events • Signal strength low – Device status events • “Last gasp”, • “Power restore” events
- 16. 16 So what are the Smartmeter use cases driving ROI for utility industry ?
- 17. 17 Tamper Loss Spotter - Bird’s Eye View
- 18. 18 Tamper loss spotter- Worms Eye View
- 19. 19 Dynamic electric pricing •Time of use rates •Encourage consumers to use less power a peak rates •Reducing the need for expensive generating stations that only operate for brief periods.
- 20. 20 Analyzing frequent Smartmeter sequences X 1 2 3 4 Day-1 9:12 am Fuse blowout Day-6 3:15 pm Line Maintenance Inspection Day-9 10:15 pm Transformer or Eco system device recalibration Day-11 3:15 pm Three 2 sigma Voltage Surge Event occurred Within 24 hours Day-12 3:15 pm Power Outage Breaks down X X X X X X X X X X X 1. Is it Electrician ? 2. Is it Calibration ? 3. Is it Load ?
- 21. 21 Chillers + Boilers Heavily instrumented Flow sensors Steam pressure sensors Temperature sensors Status sensors
- 22. 22 Command centres : Real time streaming sensor data
- 23. 23 M2M Big Data Solution architecture 1 2 3 4 Command centre Field Technicians Head office Design Process Optimizers Reduce Massive Risks + Unlock Process efficiency Digital Oil Field Smart Buildings Smart Meter grid Telecom R E A L T I M E I N G E S T I O N Deep Machine Learning Complex Machine Vectorisation Gamification and Habit design Triangulated Markers Massively Scalable Sensor Data Ocean Real time sensor event pipelines
- 24. 24 What are the 3 important “Gotchas” for M2M + Big Data Analytics from the trenches ?
- 25. 25 Gotcha-1: TRIANGULATE SIGNALS ACROSS DEVICES/MANUFACTURERS ! Cast a wide net in “sensor data ocean” to triangulate on multi device signals across multiple real time event streams
- 26. 26 Gotcha-2 : Machine learning @ scale Harvest signals and patterns from streaming sensor data- 3 techniques • Sequence • Frequent sequences • Large graph • Text mining
- 27. 27 Gotcha-3 : Gamification + Habit Design Applied Behavior Psychology in Design Gamification + Habit Design Deep Machine Learning Hadoop Cluster + NoSQL Persistence Real time streaming data pipelines 80 %Of work
- 28. 28 When you flush sensor event data lives are at risk !!! Sensor event streams being flushed Lives being saved