Restricted - Confidential Information© GSM Association 2010

Energy Efficiency Benchmarking for Mobile Networks

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Contents

Objectives and Benefits

Methodology

Example Output From Pilot Phase

Our Offer

Next Steps

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Objectives of the Energy Efficiency Initiative

Develop a benchmarking methodology, KPIs and benchmark outputs which allow mobile operators to:

– benchmark themselves externally and internally, and

– reduce energy consumption, emissions and costs

Coordinate with industry and regulatory stakeholders so that the benchmarking methodology is adopted as a global standard by the industry

Ensure that data confidentiality is preserved

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Benefits for Operators

Benchmark networks against peers, develop insight into energy use and target cuts in energy consumption

- A calculation of potential cost and CO2e savings for each network

Participate in a large dataset which leads to improved regressions and statistical significance and more useful results

Demonstrate a commitment to energy and emissions reduction, which will have a positive impact on regulators, investors, customers and other stakeholders

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Contents

Objectives and Benefits

Methodology

Example Output From Pilot Phase

Our Offer

Next Steps

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Methodology

Measure mobile network energy performance by country: – Energy per mobile connection– Energy per unit mobile traffic – Energy per cell site– Energy per unit mobile revenue

Compare networks anonymously

Normalise for variables outside the energy managers’ control for example country, geography and technology factors. This process, which uses multi-variable regression analysis, enables like-for-like comparisons

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Normalisation

Country factors– Population density– Urban / rural population split– Number of cooling degree days– Topology

Market factors– Market share– Traffic, both voice and data– Geographic coverage– Population coverage

Technology factors:– 2G / 3G split– Diesel vs. electricity

consumption

Other– Data accuracy

Larger data set enables normalisation of multiple factors whilst retaining statistical significance

General availability and accuracy of data is important, whether from operator or public domain, e.g. country and market factors

Data that is difficult to gather e.g. age of legacy kit, cooling methods and number of transceivers can be progressively included as it becomes available

Multi-linear regression techniques test intuitive assumptions about variables

Factors must be grouped in a way that makes sense when looking for a linear relationship

EXAMPLE FACTORS IMPLICATIONS

There are many factors that could impact energy efficiency

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Contents

Objectives and Benefits

Methodology

Example Output From Pilot Phase

Our Offer

Next Steps

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Example: Energy Per Connection

Regression analysis captures the impact of technology by using “% connections that are 2G” as one of the factors

Numerous regressions have been tried and a good fit has been obtained from the following factors to explain variations in energy per connection:

– % urban population / % country population covered– Square kilometres covered / connection– % connections that are 2G– % energy from diesel

Other variables, such as cooling degree days, can be added when the data set is larger

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Prior to any “normalisation”, the spread of energy per connection across countries can be quite high

Operator XMobile operations average electricity and diesel usage per connection, 2009

Source: Operator X, GSMA data and analysis

A B C D E F G H I J K L

kWh per connection

Diesel usage

Electricity usage

Country

DISGUISED EXAMPLE

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The first level of normalisation is a simple regression against one variable, for example the % of 2G connections

Operator XScatter plot of electrical and diesel energy usage per connection versus % 2G connections, 2009

Mobile operations diesel & electricity usage per connection (kWh / connection)

Country A

Country B

% 2G connections of all mobile connections

Country CCountry D

Country E

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However, regressing against several variables at the same time gives a true “normalisation”

Source: Operator X, UN, GSMA data and analysis

Line of best fit:

R2 = 90%

Mobile operations diesel & electricity usage per connection regressed against:- % Mobile operations diesel usage of total diesel and electricity usage- % 2G connections of all mobile connections- Geographical area covered by all MNOs per connection- % urban population / % population covered by all MNOs

Operator XDeviation from line of best fit: average electrical and diesel energy usage per mobile connection, 2009

kWh / connection

AB CD EF G HI JK L

Country

DISGUISED EXAMPLE

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The value comes from assessing the implications with energy managers based on their knowledge of other internal factors

After normalising for these factors, certain countries have high or low energy per connection:

– High: Countries F, B, I, D– Low: Countries E, J, L, H

Various factors could explain the over or under-performance of different countries:– Energy efficiency of network equipment – Network frequency– Network design– Cooling method– Number of cooling degree days – Topology (though analytically not a factor here)– Traffic (though analytically not a factor here)– Data accuracy (to be determined)

Improving the energy efficiency of countries F, B, I and D to the average (post normalisation) will reduce energy costs in those countries by XX% on average

DISGUISED EXAMPLE

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An anonymous comparison against other operators will allow greater insights for energy managers

Line of best fit:

R2 = 90%

Mobile operations diesel & electricity usage per connection regressed against:- % Mobile operations diesel usage of total diesel and electricity usage- % 2G connections of all mobile connections- Geographical area covered by all MNOs per connection- % urban population / % population covered by all MNOs

Operator XDeviation from line of best fit: average electrical and diesel energy usage per mobile connection, 2009

kWh / connection

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Canada

Mexico

South Africa

Source: MNOs, GSMA data and analysis

France Italy Japan

Operator X

Other Operators

Key Regression variables

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Contents

Objectives and Benefits

Methodology

Example Output From Pilot Phase

Our Offer

Next Steps

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Our offer to MNOs

GSMA will:

Collect data from MNOs on electricity and diesel energy use by country for 2009 by cell site, connection, unit traffic and unit revenue

Gather external datasets that might explain variations in energy consumption along technology, country factor and market dimensions

Normalise energy use data to compare like-for-like, using multi-linear regression analyses

Feed back results bilaterally to each MNO participant and refine analyses

Combine data with other participant MNO data and feed back benchmarking results to each MNO participant

Provide each MNO with an estimate of the potential cost savings available as a result of energy reduction

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Contents

Objectives and Benefits

Project Status

Our Offer

Example Output From Pilot Phase

Next Steps

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Next Steps

Agree participation of MNO, including which data can be anonymised and pooled. Start gathering data (and sign confidentiality agreement)

Data required are not onerous. We need, by country for 2009:– Mobile network electrical energy usage and diesel energy usage– Number of physical cell sites, number of mobile connections– Minutes of mobile voice traffic and bytes of mobile data traffic– Mobile revenues

Analyse and benchmark MNO countries as per the example shown for– Energy per mobile connection– Energy per unit mobile traffic – Energy per cell site– Energy per unit mobile revenue

Review the resulting analysis and conclusions with MNO

Re-check regressions using large data set, run regressions with more variables, and run regressions separately for developed and emerging market countries