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Market Research Report

Electricity Theft and Non-Technical Losses: Global Markets, Solutions and Vendors

Published by Northeast Group, LLC Product code 499449
Published Content info 58 Pages plus Dataset
Delivery time: 1-2 business days
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Electricity Theft and Non-Technical Losses: Global Markets, Solutions and Vendors
Published: May 9, 2017 Content info: 58 Pages plus Dataset
Description

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Globally, utilities lose $96 billion per year to non-technical losses - typically electricity theft, fraud, billing errors, and other lost revenue. The effect of these losses can be crippling, driving up electricity prices for paying customers, starving utilities of the resources for future capital investment, and in many cases creating financially unsustainable utilities or draining governments of subsidies that could be used to modernize the country's infrastructure. Until recently, there were few effective solutions for this problem. Labor-intensive premise inspections and account auditing often costs more than the actual value of the losses, enforcement is always challenging, and previously AMI metering was prohibitively expensive in many countries. This is now changing, with AMI plans in the works across all regions of the world.


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Furthermore, sophisticated software and analytics can use real-time data from AMI meters and other grid infrastructure to pinpoint the source of non-technical losses. These solutions are spreading from developed countries where they are in many cases already well established to emerging market countries where the problem is significantly larger and critical to the fundamental health of many countries' utilities. This will create a multi-billion dollar market, with companies from small software startups to the largest metering vendors and system integrators seeking to gain a piece of the market.


Key questions answered in this study:

  • How much does each country across the world lose to non-technical losses in terms of dollars, percentage lost, and dollars lost per customer?
  • What are the most cost-effective solutions for reducing non-technical losses?
  • Who are the leading vendors and how are their solutions differentiated?
Table of Contents

Table of Contents

  • i. Executive summary
  • 1. Quantifying the electricity theft problem
  • 2. Loss-reduction solutions
  • 3. Market forecasts
  • 5.1 AMI forecast
  • 5.2 Revenue protection analytics forecast
  • 5.3 Prepaid metering forecast
  • 4. Case studies
  • 5. Revenue protection vendors
  • 6. Appendix (data for 125 individual countries)

List of Figures, Boxes, and Tables

  • Electricity theft and non-technical losses: Key takeaways
  • Figure 1.1: Non-technical losses by percentage and value
  • Figure 1.2: Top 30 countries for non-technical losses in terms of value
  • Figure 1.3: Non-technical losses by region
  • Figure 1.4: Comparison of NTL and revenue protection analytics investment
  • Figure 1.5: Top 20 countries for NTL on per-capita basis
  • Figure 2.1: Comparison of loss-reduction activities
  • Table 2.1: Non-technical loss reduction options
  • Figure 2.2: Analytics-based loss-reduction solutions
  • Figure 3.1: AMI forecast by region (cumulative)
  • Figure 3.2: Regional residential electricity meter market sizes in 2017
  • Figure 3.3: Regional residential electricity meter market sizes in 2027
  • Figure 3.4: Revenue protection analytics forecast by region (cumulative)
  • Figure 3.5: Combined prepaid metering (thick and thin) forecast by region
  • Figure 3.6: Current state of global prepaid electricity metering
  • Table 3.1: Largest prepaid metering growth markets
  • Figure 4.1: Revenue protection case studies
  • Figure 4.2: Expected costs and savings for Energia+ at Eletrobras
  • Figure 4.3: Billing changes after revenue protection program at SMUD
  • Table 4.1: Non-technical loss reduction solutions at JPS
  • Table 4.2: Donor loss-reduction initiatives in Mozambique
  • Figure 5.1: Revenue protection analytics vendor landscape
  • Table 5.1: Other revenue protection analytics vendors
  • Table 6.1: Global non-technical loss data
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