NRW TECHNIQUES & SMART METERING TO IMPROVE OPERATIONAL EFFICIENCIES ACWUA, AMMAN – JORDAN, JANUARY 2013

WORLD DEMAND FOR WATER BY % Sources: US Energy Information Agency and United Nations IN 48 COUNTRIES WILL FACE SCARCITY BY billion

THE NON REVENUE WATER CHALLENGE Source: Global Water Intelligence (UK), RiverNetwork (Portaland)

How to addresses the waste? WATER CONSERVATION Manage the increasing demand for water Reduce water losses Engage consumer in conservation efforts

»Itron Program to address Water Loss Reduction on Apparent loss INTERNATIONAL RECOGNIZED CLASSIFICATION 10-12% of Total Volume

NRW LOSSES SEGMENTATION 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Volume (%) BILLED AUTHORIZED CONSUMPTION Measured, low uncertainty (+/- 2%) METER INACCURACIES AND DATA ERRORS Estimated, high uncertainty (+/- 30%) Low investment REAL LOSSES Calculated by substraction, mid uncertainty (+/- 17%), High investments UNBILLED AUTHORIZED CONSUMPTION Estimated, high uncertainty (+/- 50%) UNAUTHORIZED CONSUMPTION Estimated, high uncertainty (+/- 50%) Apparent Losses Represent 30% of overall losses (NRW)

SOURCES OF APPARENT LOSSES – METERING METERING BILLING READING  At Start: Inadequate selection  With time: Ageing, loss of accuracy  Typing error  Purposely mis-reading  No reading  Wrong index  Overbilling  Revenue loss 7

METERING ADDED-VALUE CHAIN How data becomes useful Intelligence from the very beginning and at every step. Data actionable starting with its creation.

12 Nov Backflow - index Leakage detection High Peaks Values above threshold index Values below threshold index over & under sized meter flags logging intervals Automatic time synchronisation Meter reversed alarm Meter blocked alarm Time of use index Event log book SMART = no longer just a meter reading

868 MHz 200mW/38400 B GPRS/Ethernet FTP Server 433MHz 10mW/2400 B Internet/ Intranet link Utility Mainframe (Billing System / Customer Information System) Customer Care software Data is automatically filtered and exported to the data center, billing system or knowledge applications. EverBlu Software SYSTEM OVERVIEW – Medina City EverBlu Collector Cyble, Pulse and Option Board EverBlu Access Point Customer forecasting software

Fixed Data Collection System (FDC)

Add Access Point to communicate with collector

Total consumption in 21\7\2010 = m³. The meter was reversed in 27\7\2010. Then the meter was reinstalled again in the right position on 15\8\2010 at 10am. Total consumption = m³ Total volume consumed during the time the meter was reversed is m³ (29 m³ for the above mentioned duration + 95 m³ accumulated backflow) This Fraud was costing the Utility 1,140 m³ / year TAMPER/FRAUD Detection This example is about a real life tampering situation where the water meter was Re-Installed by the tenant to reduce the large consumption index of the meter.

LEAKAGE EXAMPLE Vacant Premises

Handhelds HHU collection software Meter Data Collection Energy Forecasting Distribution Asset Optimization Revenue Protection Customer Care Meter Data Management Prepayment Managed Services Metering

21 Data workflow A city with 100ku Customers: 24x365x100,000 = 876 Million Reads per Year !

DMA MANAGEMENT TO REDUCE LOSSES Customer Care Software – My Meter Groups Screen Aggregated District Feb 15 12:00 AM All readings are time synchronized District Meter

Water Utility X: 500k customers Production 110 Million m3/year Total Billed 70 Million m3/year Top 1Ku customers consume 28 Mm3/year Itron program focus on the top 1k customers Investment €3 Million over 3 years Additional invoicing of 5 Million m3/year Value = 5 Million €/year additional Billing Program duration 3years: €15 Million Network efficiency BeforeAfter 70/110 = 63.6% 75/110 = 68% NRW 36.4% NRW 32% REAL LIFE CASES (UTILITY NAME CONFIDENTIAL) Water Utility Y: 240k customers all residential Production 28 Million m3/year Total Billed 16 Million m3/year Itron program focus on 120k customers Investment €6 Million over 8 years Additional invoicing of 2.5 Million m3/year Value = 3.8M€/year additional Billing Program duration 8 years: € 30 Million Network efficiency BeforeAfter 16/28 = 57% 18.5/28 = 66% NRW 43% NRW 34%

Thank You