Utility Industry Business Drivers, Results

Big Data Analytics Utility Industry Business Drivers, Results December, 2016

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |

The Pace of Change: Accelerate Innovation

Required power sector investments are massive • 2014-2035 cumulative investment of $16.4 trillion

needed globally IEA

Infrastructure improvements can’t wait • Power outages up 285% since 1984; U.S. ranks last among

the top nine Western industrialized nations in the average length of outages Bloomberg

Climate-related disasters increasingly costly • 7 out of 10 most costliest storms in US history occurred

between 2004 and 2012 President’s Council of Economic Advisers

Customers expect more…for less • Outage communications, service request updates and

high bill alerts…all at a low cost Accenture

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New Sensors / Distributed

Computing on Transmission and

Distribution Lines alert operators, fix

problems, integrate large-scale

renewables generation

Smart Meters and Home Networks

help customers use energy wisely,

mitigate peak demand, integrate

local renewables

Strong Tailwind of Technology Trends Connected Customers, Connected Networks, Connected Assets

Generation

Transmission

Distribution

Consumers

BIG DATA

MOBILE CLOUD

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Common Business Drivers for Utility Companies

• Fraud Detection

– Predictive modeling of potential fraud in billing, meter tampering, power diversion

• Assets: Smart Meter and Grid Operations

– Preventive maintenance, determination of defective meters and equipment

• Energy Efficiency / Demand Response

– Targeted campaigns to reduce peak loads

– Grid utilization planning & energy trading

• Supply Chain Management Logistics

– Route optimization and optimized maintenance

– Optimally located supplies, distribution points

• Weather / Event Planning & Crew Deployment

– Optimal staging of manpower and equipment in response to changing weather patterns

– Safety analysis and optimization

• Customer Experience

– Call Center – anticipate reason for inbound calls, provide CS reps with heads-up display

– Customer Profiling – personalize campaigns, bill formats, preferred notification channels

– Intelligent Call Routing – matching customers with ‘best fit’ CS representatives

– Recommendations – target value added services, new programs, products, services

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Preventing Theft of Service (Fraud) Objective: Detect fraudulent power usage in current residential accounts, disintermediate theft of service activity, recover revenue and significantly reduce ‘false positives’

• Oracle Big Data Cloud Services, including: Cloudera Hadoop Dist., Connectors, Oracle R Advanced Analytics for Hadoop, Spatial & Graph, Storage and Data Integrator

• Field investigative teams dispatched based upon basic system alerts generated by 2 way power meters, which often (80% of the time) resulted in ‘false positives’

• Advanced Analytics teams struggle to access enterprise data, grain (detail) of data required is not available in DW and they spend 60-70% of time gathering, preparing data for analysis

• Executive leadership had investigated point solutions, but wanted to own the competency and leverage internal IT to establish a foundational capability (Data Lake / Data Lab) for broader enterprise analytic capabilities

Business Challenge / Opportunity

Proposed Solution

Customer Example

Regional Utility focused on Operational Efficiency and

Revenue Recovery from Theft of Services provided

Potential Benefits

• Improving fraudulent power usage detection in residential accounts by 80x viewed as very conservative Yr. 1 estimate

• Over a 3 year horizon, potential incremental revenue recovery from improved fraud detection and reduced ‘false positives’ ranged from $2.9m to $4.9m in benefits, and a 1,056% ROI

Analytic Sprint Results

• Developed behavior detector and false positive rules engine prototype for detecting potential instances of fraud. Achieved high level of confidence in back testing v. known fraud cases.

• Demonstrated the ability to easily add new data, business rules and scale analytic model beyond current sampling process

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Preventing Infrastructure Damage from Pipe Leakages Objective: Develop a flexible AI system to guide infrastructure operations and strategic planning that provides mathematically concrete inferences and predictions

• Oracle Big Data Cloud Services, including: Cloudera Hadoop Dist., Connectors, Oracle R Advanced Analytics for Hadoop, Spatial & Graph, Storage and Data Integrator

• Every year billions dollars of economic damage, environmental damage, and opportunity costs are caused by preventable pipe leaks

• Pipe Leaks cause pipes to burst, water main breaks, and destabilize surrounding pipes, in addition to being a threat to public health

• Firm's existing system was too theoretical, non-data driven, relied upon human expert judgment, and most importantly - couldn't accurately predict whether a pipe would leak


Proposed Solution Customer Example

One of the largest public water utilities in the US focused on reducing the

economic and environmental impact of pipe leakages

Potential Benefits • Billions of dollars in savings in infrastructure costs, reduction in

false positives/false negatives, technological enablement, and the development of new manufacturer purchasing standards

Analytic Sprint Results

• Developed 100% data-driven AI system that can predict whether a pipe would leak with 99.9% accuracy, significantly reducing the cost of both false positives and false negatives

• Demonstrated ability of AI system to provide mathematically concrete casual inferences and predictions, confirmed expert knowledge, and taught experts things that they did not know

• Enabled the firm to perform infrastructure stress testing and scenario modeling using the AI system's probabilistic outputs as inputs and move from a reactive stance to a proactive one

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Optimizing Data Management, New Analytic Capabilities Objective: Reduce data management costs by optimizing workloads across analytic platforms, while simultaneously ‘democratizing’ new data sources and introducing new analytic capabilities

• Oracle Big Data Appliance

• ETL & Data Migration Services (partner)

This utility client is working to reduce their cost of data management and introduce new analytical capabilities to their enterprise.

• Reduce significant $ spent on ETL processing on the current legacy data warehouse (DW)

• Integrate new data sources such as Smart Meters into new product offerings and pricing on the grid



Potential Benefits

• By expanding their existing Oracle BDA footprint to support the legacy data warehouse ETL workloads and introducing capacity for new data, they will reduce the long term capital and opex costs of their existing Data Warehouse (DW)

Big Data Optimization Results

Client was looking for guidance on sizing and capacity questions related to legacy DW and emerging multi-structured data needs. Within 3 weeks, Oracle’s Big Data Optimization team provided: • Planning and Discovery • DBQL & PDCR Interrogation • Documented Data Flow • Key findings and Recommendations

The largest provider of electricity in deregulated

Western US market, serving 14 million customers. Also operates a regulated gas and

water utility.

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Enterprise Data Lab, Enabling New Analytic Capabilities Objective: Enable new analytic capabilities and business insights to improve operating efficiencies, asset management, grid reliability and overall customer experience

• Enterprise Data Lab, consisting of: Oracle Big Data Discovery and Oracle Big Data Appliance

Client enterprise steering committee established and drove development of a 3 year strategic roadmap to support Utility of the Future growth initiative. • Define enterprise analytic strategy and required technology

ecosystem to support the long-term strategic objectives • Design reference architecture and phased implementation

approach to integrate all domain subject areas



Potential Benefits

• By establishing a Data Lake and Data Lab, client will be able to finally collect vast amounts of meter data and customer data, enabling new analytic capabilities and insights to improve operating efficiencies, reliability and revenue recovery

Big Data Business Use Cases

• Predictive maintenance and remote monitoring health of meters, transformers, comms.

• CVR - Voltage optimization, leveling the voltage profile

• Real time asset health index scores (system and SCADA data)

• Water leakage detection and usage monitoring

• Customer Trading – energy usage forecasting and arbitrage

The largest electric holding company in the US by revenue, the largest

regulated utility in the US with approximately 10

million Eastern customers.

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Utility Industry Business Drivers, Results