TEA Connected Analytics JEA Zero Consumption Pilot Project
William Clarke, Executive Dir., Strategic Innovations & Analytics, TEA
Dr. Jian Hu, Research Scientist, Strategic Innovations, TEA
Kent Mathis, Manager of Utility Analytics, JEA
Today’s Discussion Topics
• What is Connected Analytics? (Bill)
• Description of the JEA Zero Consumption Pilot Project (ZeroCon) problem statement (Kent)
• Machine Learning and how it was used to solve the ZeroCon problem (Jian)
• Success! (Kent/Jian)
• What’s the future? (Bill)
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TEA’s Five-year Strategic Priorities
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0
Cost and Efficiency
Culture of Performance
Organic Growth
Emerging Services
Industry Solutions
Strategic Growth
TEA will control costs and improve
efficiencies to increase value.
TEA will promote a performance-based culture that inspires
innovation and excellence.
TEA will add new clients and market existing
services to community-owned
entities.
TEA will explore, create and expand services that add value to clients.
TEA will create value through structured collaboration and
coordination with clients.
TEA will evaluate strategic opportunities
to expand market reach or acquire resources.
Foundational Priorities Building Priorities
Connected Analytics Enablers/Drivers
• Modestly sized data sets
– Appropriate for economies-of-scale approach
• Existing Analytics Group
– Easily expanded to encompass the analysis of large smart meter volume data
• Trust relationship between Members
– enables the sharing of market and operationally sensitive data
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Utility Analytics Value CurveV
alu
e
Time
OMG!Geez we have a
lot of data!
Data Fortress
Data stored, secured and
available
Basic ReportingAnswers “what
happened?” BUT not intuitive, limited
presentation
Business Intelligence
Dashboards, dynamic data use.
Answers “what happened?” now
more intuitive
PredictiveModeling and
planning based on historic data
ExecutionLeveraging real and near real-time data
Business
TransformationBusiness process change
initiated by analytics-derived information
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2
3
4
5
6
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Prescriptive Analytics
Predictive Analytics
Reporting Analytics
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Solution Design Components
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Data Scientists & Analysts
eSmart Systems
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eSmart Systems
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Today’s Discussion Topics
• What is Connected Analytics?
• Description of the JEA Zero Consumption Pilot Project (ZeroCon) problem statement
• Machine Learning and how it was used to solve the ZeroCon problem
• Success!
• What’s the future?
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One example of using smart meter (AMI) data to improve operations and recover lost revenues
JEA ZERO CONSUMPTION PROJECT
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JEA Overview
• JEA (formerly Jacksonville Electric Authority)• Originally a municipal ‘electric’ agency that started in 1895 • In 1997 JEA took over water and sewage for the city • About 2,300 employees
• Service Territory• 900 square miles• Four counties in NE Florida• Electric, Water, Sewer, Reclaim Water, Natural Gas, Chilled
Water
• 8th Largest Municipal in the US *(APPA, Customers Served and Electric Revenue)
150,000
200,000
250,000
300,000
350,000
JEA Service Territory
FY16FY15 FY15 FY16 FY15 FY16
443,705 451,788
325,
352
333,
139
250,
974
257,
719
Average Number of Customer Accounts
Electric System Water & Sewer System
Water
Sewer
Trade, Transportation
& Utilities 21%
Professional & Business Services
15%Education & Health Services
15%
Leisure & Hospitality
13%
Total Government
11%
Financial Activities
9%
Construction 6%
Other Services 5%
Manufacturing 5%
Source: “Florida Nonagricultural Employment – Most Recent 12-Months”
• Located in Northeast Florida, the Jacksonville Metropolitan Statistical Area (MSA) has an estimated population of 1.4 million according to the Census Bureau’s latest estimate1
• The Jacksonville MSA saw a 7.9% increase in population from April 1, 2010 to July 1, 20151
• Service territory also includes a small number of customers in neighboring St. Johns, Nassau and Clay Counties
• The local economy is made up of a diverse mix of industries
1 U.S. Census Bureau, Population DivisionAnnual Estimates of the Resident Population as of July 1, 2015
250,000
300,000
350,000
400,000
450,000
443,
705
451,
788
“Highest Customer Satisfaction with Business Electric Service in the South among Midsize Utilities.”
JEA received the highest numerical score among 13 midsize utilities in the South in the J.D. Power 2016 Electric Utility Business Customer Satisfaction Study, based on 21,852 responses, and measuring the experiences and perceptions of business customers surveyed March and November 2015. Your experiences may vary. Visit jdpower.com
JEA Electric System Overview
• Net Assets In-Service: $3.1B
• Power Production Assets
‒ 6 Plants, 18 Units
‒ Net Capacity: 3,414 MW (3,743 MW winter)
‒ Fuel Sources: Oil, Natural Gas, Coal, Petroleum Coke
‒ Small amount of renewables (solar, landfill gas)
• Transmission System
– Voltage Levels (KV): 500, 230, 138 & 69
– 745 Miles of Transmission
– 74 Substations; 200 Transformers (high side >= 69kV)
• Distribution System
– Voltage Levels (KV): 26.4, 13.2 & 4.16
– 336 feeders (214 – 26.4kV; 95 – 13kV; 27 – 4kV)
– 6,760 circuit miles (45% Overhead; 55% Underground)
– 102,600 transformers, 200,900 poles
1 The average dispatch prices at maximum load for each unit from 10/1/2015 through 9/30/20162 NS CFB burned a blend of pet coke and coal during FY16
FY16 Dispatch Stack1
Cap
acit
y (M
W)
0
500
1,000
1,500
2,000
2,500
3,000
3,500
Scherer 4 (Coal)$24.95/MWh
Combined Cycle (Gas)$19.85/MWh
NS CFB (Pet Coke & Coal)2
$22.90/MWh
SJRPP (Coal)$33.12/MWh
Simple Cycle CT (Gas)$27.14/MWh
NS3 (Gas)$27.52/MWh
Water & Sewer System Overview
Water System• 19 major and 18 small water treatment plants and
two re-pump facilities
• 134 active water supply wells, 4,449 miles of water distribution mains and total finished water storage capacity of over 70 million gallons
• Two major and four small distribution grids
Sewer System• Approximately 3,898 miles of gravity sewers and
force mains
• 1,375 pumping stations, 790 low pressure sewer units, and 11 treatment plants currently ranging in rated average daily treatment capacity from approximately 0.2 to 52.5 MGD
The Issue
From January 2011 through February 2016, JEA Water Meter Services team completed 30,316 truck rolls related to Zero Consumption. Of those truck rolls, 24,195 were “wasted” truck rolls because no work was needed. This translates to approximately $1.1M expense related to unnecessary truck rolls.
The project team consisting of JEA, TEA & eSmartinvestigated the historical truck rolls and developed an algorithm to better identify broken meters. The goal was to reduce wasted truck rolls to 20% or less.
Problem Statement
• Leverage AMI data to reduce the number of wasted truck rolls for suspected broken water meters from 80% to 20%
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Current StateFuture State
Understanding the Problem: Why is the Meter Showing No Consumption?
No usage for 3 months in a row
Meter dead/brokenMeter stuck or in
process of breakingOccupied but no
water consumptionVacant
Meter dead/broken: normal water usage before the zero-con, normal electricity usage
Meter stuck or in process of breaking: normal/intermittent water usage before zero-con, normal electricity usage
Vacant: low electricity usage & variance, lots of zero water reads, high turnovers (rental?)
Occupied but no water consumption: no water usage (e.g. well in backyard) with normal electricity usage
Current ‘Broken Water Meter’ Process
Monthly Billing Reads
Create truck roll ticket
Working Properly?
List meter as working
Remove from report
for 1-yr
Repair/replace meter
Route to billing dept.
Repeat process
next month
>90 days Zero-con?
Billing Corrections?
N N
N
Y Y Y
Y
N
Y
Electric service at location?
>300 kWh at
location?
N
Pilot Objectives
• JEA and TEA would pursue two distinct objectives:
– Improve the accuracy of identifying zero consumption meters
– Determine if TEA adds value by providing this analytics service
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Project Timeline
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Project Kick Off
June July Aug Sept Oct Nov Dec
Data Collection
Analysis & Modeling
Initial Field Testing
Model Enhancements
Final Field Testing
Today’s Discussion Topics
• What is Connected Analytics?
• Description of the JEA Zero Consumption Pilot Project (ZeroCon) problem statement
• Machine Learning and how it was used to solve the ZeroCon problem
• Success!
• What’s the future?
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A Supervised Machine Learning Approach
• Data gathering: get all data in one place, past truck rolls, water/electricity consumptions, billing/account data, etc.
• Data Engineering: Re-construct data scenarios around the truck rolls, identify featuresand statistical correlations
• Model Building: build and cross-validate the model
• Field testing and Deploy
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Sample Statistical Studies
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Corr(broken, elec_mean_12m) ~0.47 Corr(broken, turnovers) ~ -0.178 Corr(broken, meter_age) ~0.04
Machine Learning Models
• Decision Forest
• Supporting Vector Machine
• Neural Network
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Model Building
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• We leverage Microsoft Azure ML Studio for model building and cross-validation
• Model is exposed as a web API for integration
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Precision = TP/(TP+FP) (% marked meters are truly dead)Recall (or TPR) = TP/(TP+FN) (% of dead meters marked)FPR = FP/(FP+TN)F1-Score = 2 * precision * recall / (precision + score)
Cross-Validation - NN vs SVM
NN Precision/Recall
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Precision: % marked meters are truly deadRecall: % dead meters markedF1-Score = 2 * precision * recall / (precision + score)
System Integration
• Logic is exposed as a web API integrated in eSmart connected grid
• ML job can be scheduled on daily or weekly basis
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Today’s Discussion Topics
• What is Connected Analytics?
• Description of the JEA Zero Consumption Pilot Project (ZeroCon) problem statement
• Machine Learning and how it was used to solve the ZeroCon problem
• Success!
• What’s the future?
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Field testing - Ready to Go
Field TestingOne worker & Six Supervisors
Final Field Testing
• December 8-10, 2016
• Visited 102 meters by three different crews
• Correctly identified 89 broken meters
• 87% accuracy• Exceeded project goals
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Recap• This project consumes lots of JEA smart-meter data
• Built on top of Microsoft Azure and eSmart Connected Grid, used both open source and proprietary data science technologies
• Finished in six months
– Adopted agile methodology - fail fast, learn quickly
– Successful collaboration between TEA, JEA and eSmart (Norway) across three time zones
• Two field testings conducted
– 89/102 new meters were identified correctly in 12/6 field trip —> 87% accuracy
– Reduction of Unnecessary Truck rolls > 90%
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Overall Benefits to JEA
• Reduced wasted truck rolls
– Field crews redirected to other priorities
– Morale improved
• Uncovered unknown meter issues
• Revenue recovery
• Framework for additional uses cases
– Data access & security
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Financial Benefits to JEA
• One-time Benefit: ~$180K– Over 4,000 zero consumption meters prior to year 2016.
Estimated that 10% (400 meters), can be recovered
• Annual Revenue Gain: ~$220k– 150 meters per month recovery for water & sewage revenue
• Annual Cost Avoidance: ~$270K– 72% truck roll reduction in total truck rolls (5400/7500), or 90%
reduction in unnecessary truck rolls (5400/6000)
Net value to JEA over three years: ~ $1.3 million
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Today’s Discussion Topics
• What is Connected Analytics?
• Description of the JEA Zero Consumption Pilot Project (ZeroCon) problem statement
• Machine Learning and how it was used to solve the ZeroCon problem
• Success!
• What’s the future?
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What’s Next?
• Well-formed, scalable structure • Business Plan
– Expand services to Members & Partners– Joint marketing with eSmart Systems
• Project Exploration • Fraud detection • Asset management• Business information dashboards• Long-term load forecasting• Revenue enhancement• Improved utility customer interaction• Utility planning & operations
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Questions
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Potential Use Cases – SideBar –
Potential Use Cases - Corporate• Business Information Dashboards
(AaaS)– Management metric dashboard– Customer dashboard– Municipal dashboard/portal– Operations dashboard
• Long-term load forecasting (AaaS)– Load forecasting by customer class– Residential load shape
segmentation– Residential engagement
segmentation– Solar forecasting & visualization
• Rates (AaaS)– Rate design by customer class– Rate validation– Pricing optimization– Cost & learn conservation program– Pricing optimization & rate design
• Revenue (AaaS)– Anomaly detection for meter
consumption– Revenue risk model– Fraud detection– Credit and collections
• IT (AaaS)– Information management
architecture– Data governance– Cyber/data security
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Potential Use Cases – Customer Focus
• Customer engagement platform• Customer personal usage analytics• Customer profiles• Customer shut-off/turn-ons (Member function)• Customer payment systems• Interaction with customer premises (Nest technologies)• Using “Big Data” to understand and engage utility
customers (Member function)• Improving customer satisfaction with technology
applications & analytics designed to handle “Big Data”• Customer mobile• Customer segmentation
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Potential Use Cases – Planning• Demand response (reduction) (AaaS)• Energy efficiency programs (AaaS)• Transformer load management (AaaS)• SCADA/OMS/AMI outage analysis (AaaS & Member function)• Voltage visualization (AaaS)• Distribution automation placement (AaaS)• The shift from time-based to condition-based maintenance (AaaS)• Workforce optimization (AaaS)• Managing transmission & distribution assets (AaaS)• Management of distributed generation (AaaS)• Resource expansion planning (AaaS)• Transmission & distribution asset planning (AaaS)• Inventory management (AaaS)• Equipment maintenance scheduling (AaaS)• Power quality optimization (AaaS)• Artificial Intelligence using 3-D imagery to view equipment in the field without physically being in
the field (AaaS)
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Potential Use Cases• Meter health &
infrastructure– Inaccurate meter usage
(AaaS)– AMS system health (AaaS)– AMI executive dashboard
(AaaS)– Meter temperature data
analytics (AaaS)– Smart meter analytics –
health of the AMI system (AaaS)
– Meter communications infrastructure Operations (AaaS)
• Operations (Time sensitive solution)– Analytics for real-time
network operations (Member function)
– Estimated time of restoration (Member function)
– Mobile workforce (Member function)
– Storm restoration (Member function)
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Problem Statement
• Question: when a water meter continuously reports 0, is it truly broken?
– 80% of the 30,000 zero-con truck rolls were ineffective ($50 per trip)
– Many broken meters were not be identified in-time due to limited field crews - can we stop the bleeding earlier?
• Project goal: can we reduce 80% of wasted trips to 20%?
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Value
• ~$1.68M in three years
– ~$180k one-time
– ~$500k annually
• Main benefit components:
– Benefit of saved truck rolls ($50 per ~500 monthly)
– Early revenue recovery
– Non-monetary: Improved staff morale, customer satisfaction, environmental benefits
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