This document discusses the challenges facing water utilities including increasing water consumption, aging infrastructure, and water loss. It presents smart water network solutions from TaKaDu that use big data analytics to detect leaks and other water network issues. TaKaDu's algorithms learn normal network behavior patterns to predict usage and detect anomalies, helping utilities reduce water loss and improve customer service. Real-life examples are given showing how TaKaDu detected leaks and pressure changes using historic and network predictions.

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. World Economic Forum
Global risks 2015
Water Crisis – highest impact risk

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. World’s
population
x3
Water
consumption
x6
In the 20th century:

5. Lifestyle = Water
7,000 L
Cotton t-shirt
11,000 L
Hamburger
15,000 L
Pair of jeans

6. 6
We have an addiction!
We love…

7. Recognition to: ABB, i2O, Gutermann,
Incertameter, HWM, Primayer, Syrinix,
SebaKMT, Technolog

8. Utilities collect millions of pieces of
data each day

9. 1980 1990 2000 2010 2020
Intelligence
Technology
Data
Value
Progress

10. 10

11. ERP
Billing
Water
Quality
GIS
Integrated
Water Network
Management
Water
Loss
Asset
Management
Control
Room
Customer
Service
Work Order
Management

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. 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
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
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. Big Data
Cloud Computing
Smart Analytics
SaaS Model

17. SaaS paradigm shift
Set up in 5 weeks
No customer testing
Free upgrades
Free training
Web-based software
Unlimited users

18. REAL-LIFE EXAMPLES

20. Impacts customers
Company image
Huge costs!
Water loss
COULD THIS BE PREVENTED IN FUTURE?

21. Event recognition using TaKaDu

22. Early detection

23. 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

24. Appears to
be a leak
Reciprocal effect
on adjacent DMA
Breach detection using TaKaDu

26. Real-life Example: Historic Prediction
Historic
prediction
Real data
FIFA World Cup, 13 June 2014, Netherlands vs. Spain

27. Real-life Example: Network Prediction
FIFA World Cup, 13 June 2014, Netherlands vs. Spain
Network
prediction
Real data

29. THANKS