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IBM Software

Beyond smart meters:Taking analytics to utilities data

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Contents

2 The wave of change

3 Business imperatives dene the analytical strategy

3 Customer optimization

4 Demand response optimization

5 Operational efciency

6 Bringing it all together

6 Glossary

Utility companies are facing a step change in the industry from

many factors at once: social and political pressures on

generation, increasing data volumes from smart grid and

advanced meter infrastructure (AMI) technologies and

sweeping regulatory changes amongst.

Evolving technology in the utilities space has generated

unprecedented data volume and complexity which will

continue to grow as the industry continues to transform andevolve. To remain competitive utility companies will need to

change from the traditional infrastructure-led business model

to one being more information- and services-led. But utilities

are also typically vertically siloed organizations with various

departments working - and managing their data - very

independently of each other. Even within a single department

it is likely that multiple operational systems and data stovepipes

exist, yet another challenge to utility companies trying to

transition to a more efficient and rational horizontally

integrated information ecosystem.

Given this highly challenging information and dataenvironment, utilities are focusing increased attention on

business intelligence and advanced analytics to provide

data-driven decision-making as they plan and manage change.

Demand for advanced analytics is growing, necessitating an

integrated view of company data across the disparate

operational silos. Utilities achieving this data integration and

ensuing analytical capabilities can gain productivity, increase

profitability, enhance efficiency, reduce the carbon footprint,

and improve customer satisfaction.

Analytics are technologies and applications including hardware

software and services that enable utilities to transform data into

actionable insights. Todays analytics are focused on leveraging

real-time data sources and analytics, bringing together multiple

data sources, predicting outcomes instead of just reportinginformation, merging new and existing data, creating flexible

applications, and better serving the data customer.1

Analytics has been successfully adopted and applied in various

industries historically from telecommunications to retail and

utilities can leverage technology and business process changes

learned from these other industries to successfully meet the

above challenges. This means building a 360 degree view of the

business and of the customer in an environment so that analysis

can be applied at the speed business is taking place. This must be

done with the simplicity to allow adaptation to future business

needs as they are discovered and understood.

The wave of changeThe utility industry has been plagued with unforeseen hype for

the past two to three years. The growing wave of smart meter

and smart grid pilots and implementations has been

accompanied by much media and analyst attention combined

with Smart conferences in every geography, every other week

Where the concept is in terms of the Gartner Hype Cycle is

open to conjecture and debate, but its clear that we are now

beyond the early adopter phase for the AMI (advanced meter

infrastructure)/meter data management system (MDMS)

portion of the architecture.

That particular concept is proven and workable and hopefully

sufficiently scalable, but there is now a growing industry

recognition that investment in smart meters and AMI/MDMS

doesnt necessarily change the game in terms of operational

improvements and providing real business benefit. Assuming

Utility Analytics Insti tute, Annual Market and Forecast, November

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the necessary integration has been achieved into the customer

information system (CIS) and billing processes, customers are

receiving more timely and accurate billing statements, and in

some cases can view detailed usage with web portals or

in-home displays. Providing more visibility of utilization is all

well and good but there isnt sufficient detail to enable all but

the most conscientious of customers to markedly change their

energy consumption. Knowing how many kilowatts of power

your home is consuming doesnt help you to understand thatyour ancient fridge/freezer is responsible for 30 percent of

your daily consumption.

Putting the usage data into context and applying analytics

unearths new opportunities for the utility company. This

cannot be readily achieved in situ on the MDMS. The MDMS

system is optimized to support the data ingestion, validation,

estimation and editing (VEE) and related core functions.

There is therefore a need to transform the data and add

contextual information to the validated usage data for

analytical purposes this is best done with a platform

optimized for high performance analytics. The frequency orlatency with which the usage data is made available to the

analytical environment can have a significant impact on the

potential business benefit. More on this to follow.

Business imperatives define the

analytical strategyHow do you decide which analytics are the most appropriate

for any particular utility? Fundamentally, the analytical

roadmap should be determined by alignment to the companys

overarching business imperatives and prevailing regulatory/

market model. Focusing on the end customer may be

inappropriate for a utility in a highly regulated monopolistic

environment. Improving security of supply and cost/quality of

service may be a better strategy.

Across the globe the main business imperatives of utilities can

be characterized as the following:

Customer optimization

Demand response optimization

Operational efficiency

While it is essential for a utility to be competent in each of

these disciplines, it is likely that an organization will prioritize

one particular area; the prioritization being determined by the

company itself or more likely influenced by the country or

state regulator. Each market discipline will be explored in

detail in a later section.

Customer optimization

Whether competing to attract and retain profitable customers in

a competitive and deregulated market or striving to improve

customer satisfaction and eliminating complaints to the

regulator, the relationships a utility establishes and maintains

with its customers is becoming an increasing priority. Customer

optimization isnt achieved by simply buying and implementing

a CRM application; there are capabilities that need to be

developed and combined to achieve the end goal. The above

figure illustrates some of the key capabilities required to achieve

an appropriate form of customer optimization.

A successful customer optimization program requires at its

foundation a full 360 degree view of the customer. This is

established by integrating customer data from all key

operational and business systems with additional relevant data

such as credit and geo-demographic data from external

agencies. It is imperative that utilities create a proper view of

their customers as opposed to viewing them as meter points.

Although the segmentation and valuation stages may seem

most appropriate to utilities operating in a competitive market,

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such customer insight is also equally important for utilities in a

monopolistic market. This customer insight capability enables

the utility to better understand past and predict future

utilization patterns, and enables credit risk management and an

understanding of what new offers and services may be most

appropriate to specific customers. It can also be used to

determine optimal tariff strategies and demand response

programs. The segmentation scheme can also provide the basis

for determining virtual power plant (VPP) allocations,ensuring customers with similar usage, curtailment and

contractual agreements are grouped together.

Demand response optimizationDemand response (DR) is becoming widely recognized as one

of the essential disciplines that all utility companies need to

embrace given the obvious benefits in peak load shifting and

potential elimination of capital investment in additional

generation capacity. Demand response is not a capability that

can be bought off the shelf and enabled, but similar to

customer optimization, demands a series of analytical processes

to fully achieve the optimization goals. The following figure

illustrates how effective demand response can be achieved by

building on other essential analytical processes.

Successful demand response requires accurate demand

forecasting models to enable the appropriate load shifting

optimizations to be identified and executed. Data latency has a

significant impact on the accuracy and granularity of the

forecasting models. Historically utilities have forecasted loads six

weeks in advance. Although historical trends and weather

forecasts are factored into predicted consumption, historical

consumption was calculated at an aggregated level and could not

be easily apportioned across the customer base. Smart meterdata will provide granular consumption data for the whole

customer base. This data will be required near to real time: both

for load forecasting and also for the monitoring and tracking of

the demand response program execution.

Course grained forecast models can be used initially to

determine where peak consumption may occur. These forecast

peaks will necessitate the execution of more granular models to

compare hourly load versus anticipated capacity. This in turn

will determine the load shedding requirement and drives the

demand response curtailment program that identifies which

VPP/customer segments (and ultimately customers) need toparticipate in the DR scheme. Once identified, the DR

program can be executed with the appropriate curtailment

requests being issued to customers. The take rate of the

program is then monitored to ensure the required load is shed

and additional target customers contacted if there is a shortfall

in take up.

While this process is similar in concept to some traditional

marketing style programs, there is an iterative real-time

requirement to monitor take-up or defection from the

program, and extend the campaign to additional customers

until the required capacity reduction has been achieved. Ideallythe DR signals should be directed to automated recipients in

the form of smart thermostats and appliances, but provision for

actual customer interaction is also required.

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At each stage identified there is a need to iteratively execute

advanced analytical models across a combination of customer,

consumption, weather and generation capacity data. To ensure

consistency it is vital that the various analytical models are

executed on the same set of data.

Operational efficiencyThe last discipline will be common to all utilities to a greater or

lesser extent. Reducing or optimizing the cost to serve customersis an industry-wide initiative in which attainment is severely

impacted by the many seismic changes the industry is facing.

Some of the key challenges faced by the utility industry

include:

Changes in the power generation to include more

renewables;

Switch to/from nuclear generation;

Huge growth in micro-generation;

Increasing ecological concerns and carbon emission taxes;

Emerging demand for and growth in electric vehicles; Increasing credit risk challenges;

Greater regulatory pressure on the industry;

Increasing incidence of energy theft;

Aging workforce

All these are contributing to a major shift in the previously

stable cost model which the industry has benefitted from since

its inception. To achieve ongoing operational efficiencies

companies will be required to encompass significant waves of

change to their business model while continuing to provide a

cost-effective service to its customers. The ability to flex and

adapt to new and changing market models will be an essentialfuture attribute. The long decision cycle around major capital

acquisition and implementation of plant equipment is being

challenged. New technologies such as solar and wind-based

generation are gaining widespread social and political

importance, and most importantly, technological investment;

the changes that have revolutionized telecommunications and

media are about to have a similar impact on utilities. The

incumbent players must move with the times or face aggressive

new entrants who will outmaneuver them.

Very few utilities have the ability today to easily determine how

their overall business is performing. When they do it is often at

an aggregated level. This is predominately a byproduct of the

historical approach to automation; specific point solutions havebeen implemented to address functions such as asset

management, workforce management and outage management.

Each solution has deployed its own independent data model,

including commonly used data. This has led to a very siloed

approach to data management with some degree of integration

through ESB/SOA architectures. The integration option works

to a certain scale but cannot address the future demands that

smart meters and a fully instrumented smart grid present. The

data volumes that the future smart world will produce preclude

the movement of detailed usage and event data to various

operational and business applications, in some cases the

applications mandate the use of aggregated or summarized data.

Future operational efficiency is best achieved on the bedrock of

a 360 degree view of the business, where all relevant business

data is placed in a single analytical environment and the various

analytical processes are brought to the data rather than piping

the data into different pseudo operational analytical systems.

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The 360 degree view of the business can combine financial data

with consumption data to provide differing perspectives of

consumption analysis. This will be used to address immediate and

emerging requirements. It can also readily provide insight into

micro generation and workforce management. Adopting the

credo of having all the data readily available and applying differing

forms of analytics means that emerging requirements to address

energy theft, revenue protection and risk management can be

readily accommodated on the same platform, generatingexponential value out of the same set of data.

Bringing it all togetherThe three disciplines outlined above have a common theme,

namely, identifying the need for a consolidated data store

which can support different types of analytics. Much of the

data required to support each discipline is common they are

not mutually exclusive. They all have some degree of

dependency on the anticipated deluge of smart meter

consumption data and smart grid events. While other

industries are beginning to focus on Big Data and its related

challenges, utilities are faced with their own step change which,

while significant from their perspective, is well within the

bounds of well established data warehousing solutions. What is

more challenging is the lack of expertise in the utility sector in

architecting, developing and managing those more traditional

data warehousing solutions.

There is an alternative option available which provides cost -

effective scalability combined with operational simplicity. The

IBMNetezzadata warehouse appliance changed the data

warehousing industry with the launch of its IBM Netezza

Analytical Appliance in 2001.

The IBM Netezza data warehouse appliance pioneered the data

warehouse appliance market by integrating database, server and

storage into a single, easy to manage massive parallel processing

appliance that requires minimal setup and ongoing

administration while delivering faster and more consistent

analytical performance. The new family of IBM Netezza data

warehouse appliances continue to set the standard for analytical

appliances by consolidating all analytical activity into the

appliance, right where the data resides, leveraging massive

parallel processing for blisteringly fast performance.

With deep integration into IBM information management and

business analytic products as well as leading third party

reporting and analytical tools, the IBM Netezza AnalyticalAppliance can provide the integration point for the utilities

analytical requirements now and the future.

GlossaryAMI: Advanced meter infrastructure

CIS: Customer information system

CRM: Customer relationship management

ESB: Enterprise service bus

MDMS: Meter data management system

SOA: Service oriented architecture

VEE: Validation, estimation and editing

VPP: Virtual power plant

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About IBM Netezza data warehouse

appliancesIBM Netezza data warehouse appliances revolutionized data

warehousing and advanced analytics by integrating database,

server and storage into a single, easy-to-manage appliance that

requires minimal set-up and ongoing administration while

producing faster and more consistent analytic performance.

The IBM Netezza data warehouse appliance family simplifies

business analytics dramatically by consolidating all analytic

activity in the appliance, right where the data resides, for

industry-leading performance. Visit ibm.com/software/data/

netezzato see how our family of data warehouse appliances

eliminates complexity at every step and helps you drive true

business value for your organization. For the latest data

warehouse and advanced analytics blogs, videos and more,

please visit: thinking.netezza.com.

About IBM Data Warehousing and

Analytics Solutions

IBM provides the broadest and most comprehensive portfolioof data warehousing, information management and business

analytic software, hardware and solutions to help customers

maximize the value of their information assets and discover

new insights to make better and faster decisions and optimize

their business outcomes.

For more informationTo learn more about the IBM Data Warehousing and Analytics

Solutions, please contact your IBM sales representative or IBM

Business Partner or visit: ibm.com/software/data/netezza.

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