1

monitoring water networks

David KennyUK Regional Director, TaKaDu9th February 2015

Big Data in the Water Sector:Integrated Water Network Management

World Economic ForumGlobal risks 2015

Water Crisis – highest impact risk

World Economic ForumGlobal risks 2015

The International Energy Agency projects water

consumption will increase by 85% by 2035 to meet the needs of energy generation

and productionGlobal water

requirements are projected to be pushed

beyond sustainable water supplies by

40% by 2030

World’s population

x3

Water consumption

x6

In the 20th century:

Lifestyle = Water

7,000 L Cotton t-shirt

11,000 LHamburger

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

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ERP

Billing

Water Quality

GIS

Integrated Water Network Management

Water Loss

Asset Management

Control Room

Customer Service

Work Order Management

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

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

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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

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

Big DataCloud Computing

Smart AnalyticsSaaS 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 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

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

Networkprediction

Real data

THANKS