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1
monitoring water networks
David Kenny
UK Regional Director, TaKaDu
9th February 2015
Big Data in the Water Sector:
Integ...
World Economic Forum
Global risks 2015
Water Crisis – highest impact risk
World Economic Forum
Global risks 2015
The International Energy
Agency projects water
consumption will increase by
85% by ...
World’s
population
x3
Water
consumption
x6
In the 20th century:
Lifestyle = Water
7,000 L
Cotton t-shirt
11,000 L
Hamburger
15,000 L
Pair of jeans
6
We have an addiction!
We love…
Recognition to: ABB, i2O, Gutermann,
Incertameter, HWM, Primayer, Syrinix,
SebaKMT, Technolog
Utilities collect millions of pieces of
data each day
1980 1990 2000 2010 2020
Intelligence
Technology
Data
Value
Progress
10
ERP
Billing
Water
Quality
GIS
Integrated
Water Network
Management
Water
Loss
Asset
Management
Control
Room
Customer
Servic...
What is a Smart Water Network?
“A fully integrated set of data-driven components and
solutions, which allow water utilitie...
A leak is born
A smart water solution detects, measures and locates it
A job is automatically raised, prioritised and sche...
14
Are older pipes worse performers?
0
5
10
15
20
25
30
35
40
45
50
0-10 10-30 30-50 50-80 80+
Age vs repairs
all
repaired...
15
0
10
20
30
40
50
60
70
Material vs repairs
all
repaired
x1.3
x1/5
Dataset: 19 DMAs; 28k pipes; 500 repairs
%
What is th...
Big Data
Cloud Computing
Smart Analytics
SaaS Model
SaaS paradigm shift
Set up in 5 weeks
No customer testing
Free upgrades
Free training
Web-based software
Unlimited users
REAL-LIFE EXAMPLES
Impacts customers
Company image
Huge costs!
Water loss
COULD THIS BE PREVENTED IN FUTURE?
Event recognition using TaKaDu
Early detection
TaKaDu’s algorithms
• Learn network behaviour patterns
• Predict expected future behaviour
• Statistically compare reading...
Appears to
be a leak
Reciprocal effect
on adjacent DMA
Breach detection using TaKaDu
Real-life Example: Historic Prediction
Historic
prediction
Real data
FIFA World Cup, 13 June 2014, Netherlands vs. Spain
Real-life Example: Network Prediction
FIFA World Cup, 13 June 2014, Netherlands vs. Spain
Network
prediction
Real data
THANKS
Big Data in the Water Sector - Integrated Water Network Management - IWC's IT and Water Conference Rotterdam
Big Data in the Water Sector - Integrated Water Network Management - IWC's IT and Water Conference Rotterdam
Big Data in the Water Sector - Integrated Water Network Management - IWC's IT and Water Conference Rotterdam
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Big Data in the Water Sector - Integrated Water Network Management - IWC's IT and Water Conference Rotterdam

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Smart Water Networks are a reality and they are certainly the future but what is slowing their proliferation? The water industry supply chain has embraced technological enhancements in sensors, communications, analytics and control yet water utilities are reluctant to adopt them. Are we about to see the light at the end of the tunnel as Big Data technologies come to the fore or will current attitudes, particularly risk aversion, hold us back?

Publicada em: Tecnologia
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Big Data in the Water Sector - Integrated Water Network Management - IWC's IT and Water Conference Rotterdam

  1. 1. 1 monitoring water networks David Kenny UK Regional Director, TaKaDu 9th February 2015 Big Data in the Water Sector: Integrated Water Network Management
  2. 2. World Economic Forum Global risks 2015 Water Crisis – highest impact risk
  3. 3. World Economic Forum Global risks 2015 The International Energy Agency projects water consumption will increase by 85% by 2035 to meet the needs of energy generation and production Global water requirements are projected to be pushed beyond sustainable water supplies by 40% by 2030
  4. 4. World’s population x3 Water consumption x6 In the 20th century:
  5. 5. Lifestyle = Water 7,000 L Cotton t-shirt 11,000 L Hamburger 15,000 L Pair of jeans
  6. 6. 6 We have an addiction! We love…
  7. 7. Recognition to: ABB, i2O, Gutermann, Incertameter, HWM, Primayer, Syrinix, SebaKMT, Technolog
  8. 8. Utilities collect millions of pieces of data each day
  9. 9. 1980 1990 2000 2010 2020 Intelligence Technology Data Value Progress
  10. 10. 10
  11. 11. ERP Billing Water Quality GIS Integrated Water Network Management Water Loss Asset Management Control Room Customer Service Work Order Management
  12. 12. What is a Smart Water Network? “A fully integrated set of data-driven components and solutions, which allow water utilities to optimise all aspects of their water distribution system” Definition by SWAN – Smart Water Networks forum
  13. 13. A leak is born A smart water solution detects, measures and locates it A job is automatically raised, prioritised and scheduled Automated street works approval! The repair team arrives with correct schematic and equipment The leak is repaired Smart technology confirms that everything is back to normal Fantasy network – the leaky dream
  14. 14. 14 Are older pipes worse performers? 0 5 10 15 20 25 30 35 40 45 50 0-10 10-30 30-50 50-80 80+ Age vs repairs all repaired x2 x1.5 x1/4 x2 % Dataset: 19 DMAs; 28k pipes; 500 repairs
  15. 15. 15 0 10 20 30 40 50 60 70 Material vs repairs all repaired x1.3 x1/5 Dataset: 19 DMAs; 28k pipes; 500 repairs % What is the best performing material? x1/5
  16. 16. Big Data Cloud Computing Smart Analytics SaaS Model
  17. 17. SaaS paradigm shift Set up in 5 weeks No customer testing Free upgrades Free training Web-based software Unlimited users
  18. 18. REAL-LIFE EXAMPLES
  19. 19. Impacts customers Company image Huge costs! Water loss COULD THIS BE PREVENTED IN FUTURE?
  20. 20. Event recognition using TaKaDu
  21. 21. Early detection
  22. 22. TaKaDu’s algorithms • Learn network behaviour patterns • Predict expected future behaviour • Statistically compare readings to predicted behavior to detect anomalies Event recognition is based on two prediction types: • Historic prediction historical data for same area • Network prediction current behaviour across the network • Together they improve detection accuracy, reducing false alarms TaKaDu’s unique prediction algorithms
  23. 23. Appears to be a leak Reciprocal effect on adjacent DMA Breach detection using TaKaDu
  24. 24. Real-life Example: Historic Prediction Historic prediction Real data FIFA World Cup, 13 June 2014, Netherlands vs. Spain
  25. 25. Real-life Example: Network Prediction FIFA World Cup, 13 June 2014, Netherlands vs. Spain Network prediction Real data
  26. 26. THANKS

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