Presented at the Rutgers University 9th Annual Public Performance and Measurement Conference. Discusses the development of energy metrics at the CTA from scratch using the EnergyCAP software for utility billing and tracking. Also covers the creation of idling and fuel metrics.

1. Chicago Transit Authority
DEVELOPING ENERGY
METRICS AT THE
CHICAGO TRANSIT
AUTHORITY
Starting from Scratch:
Navigating and Analyzing
New Energy Data

2. • The CTA has long pursued a sustainable and environmentally-
friendly transit system
– clean buses (hybrid, low-sulfur diesel, and electric)
– regenerative braking on new trains
– solar-powered machinery
– lighting upgrades
Sustainability at the CTA
2
A fully-loaded eight-car train
replaces more than 600 cars
A full 60-foot articulated bus
replaces more than 70 cars

3. • In 2015, 6.5% ($94.5M) of the CTA’s $1.4B operating budget went
towards fuel, power, and utility payments
2015 Estimated Energy Consumption
• Energy Consumption by Volume
• If “Energy” were a department at the CTA, it would be the
sixth largest by budget
Diesel
Fuel,
$50M
Electricity
(Traction),
$30M
Electricity
(Non-
Traction),
$10M
Natural
Gas, $5M
Water,
$1M
Energy Budget
3
Commodity Volume
Diesel Gas
(Millions of Gallons)
16.5
Electricity – “L” Trains/
Traction (GWh)
421
Electricity – Other Non-
Traction (Bus, Rail, Admin,
Maintenance) (GWh)
135
Natural Gas (Dekatherms) 640,000
• Utility Costs by Type

4. • Prior to mid-2014, most energy-related work involved modeling and
field observations
– due to a lack of available, centralized, automated, digital data
• In the past two years, interest in energy measures and data has increased
– internal savings
– government rebates
– comparative metrics
– new projects
Energy Data
4

5. • In June 2014, the Regional Transportation
Authority acquired a new utility-billing
software called EnergyCAP
• Centralized system allows for monthly
uploads and tracking of electricity, natural
gas, and water bills, as well as other factors
• Comes with a standard package of reports for consumption,
billing, normalization, and budgeting
EnergyCAP
5

6. • EnergyCAP allows for benchmarking, historical trending, and bill
analysis by location, commodity, and other filters
• Provides monthly data on cost and consumption of each utility at
each location
– includes all facilities, shops, stations, and substations
EnergyCAP
6
Electricity Consumption: Howard Shop

7. • Backfilling data
– all data prior to EnergyCAP launch had to be uploaded or manually entered in bulk
– created room for data errors , affecting future trending and comparisons
• CTA billed differently for each utility
– electricity: digital, natural gas: paper
– billing schedules and periods vary, making consistent MoM analyses difficult
• Auditing is a slow, collaborative process
– interdepartmental five-person team
dedicated to auditing data for missing
bills, errors, and outliers
– requires continuous back-and-forth
with utility companies
• Too much data!
– 349 reports available…and that’s not
counting all the filter combinations
– bills are granular and difficult to parse
when fixing errors or studying trends
Problems Encountered
7

8. To develop new metrics, take systematic approach and start small
• Find a “test client”: we used Facilities Maintenance
– department had already been working on energy-savings projects
(e.g. lighting upgrades)
– it had also been gathering its own consumption data
• Pick one or two key problems to solve
– wanted to quantify savings in cost and consumption
– wanted to conduct before-and-after and ROI analyses
• Understand data availability and capabilities
– what data can we confidently provide and study?
– how can we manipulate vast amounts of data to extract
what we need?
From Data to Metrics
8

9. • Facilities Maintenance wanted to see how completed lighting
upgrades had affected electricity consumption
– this affected their budget estimates and ability to get additional
project funding
– June 2015 - June 2016: the department changed lighting fixtures at 27
stations, 6 shops, and 5 garages – a total of over 10,000 light bulbs
– for the 2017 budget, the department proposed 22 energy-efficiency
projects, at a total operating and capital cost of ~$11.3M
• We decided to calculate total post-project savings to
assess the feasibility of the budget proposal
Picking a Problem
9
before
after

10. • EnergyCAP provided historical data on electricity consumption
– first, we crosswalked ComEd account information with EnergyCAP
meter names
– next, we pulled monthly data for each meter associated with a location
at which a lighting upgrade had been completed
– then, we had to audit the data by confirming the kilowattage with
original invoices; missing or wrong data had to be fixed in the system
– finally, we associated the data output with the project timeline
in order to assess savings
Gathering Data
10

11. • Decided to present data in two different analyses
– as a scorecard & accompanying historic trend graph
Compiling Analyses
11

12. – as a series of before-and-after graphs
Compiling Analyses
12
Total kWh Comparison
Before 2,014,055
After 1,738,212
Reduction 13.7%
Total kWh Comparison
Before 986,726
After 625,894
Reduction 36.6%

13. A similar process can be used with other available energy data
• Rail idling
– worked with Substation Engineering to develop breaker data dumps using SQL queries
– data generated in minute intervals  averaged by minute to assess daily and monthly peaks
– added new idling metric to Rail Maintenance department scorecard
Expanding to Other Metrics
13

14. • Overnight bus idling
– worked with IT to develop SQL query for pulling daily idling data from buses
– analyzed total idling hours, hours per idling bus, and hours per total fleet by location
– added new idling metric to Bus Maintenance department scorecard
• kWh consumed and miles per fuel-gallon
– worked with Budget Department to gather data monthly from
Hyperion database
– added new metrics to Rail and Bus Maintenance scorecards, respectively
Expanding to Other Metrics
14

15. So, why do all this?
• Scorecards, graphs, and analyses allow for trending and YoY analyses
– study seasonal patterns
– determine effects of weather and employee behavior
• Data and metrics can be used for multiple purposes
• Savings!
– 3.6% reduction in cost (> $70k) between Jan.-May 2015 and
Jan.-May 2016 – just for Facilities Maintenance
Summary of Benefits
15
─ internal
 department accountability and
procedural change
 ROI analyses
 budgeting for new energy-
efficiency projects
─ external
 rebate/grant applications
 inter-agency comparison and data
sharing
 APTA Sustainability
Agreement

16. Next Steps
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• Continue auditing and analyzing EnergyCAP data
– conduct more year-over-year trending and benchmarking
– develop additional location-specific metrics for other departments
– expand to other utilities
– develop better communication with utility companies to assist in validating data
– delve into greenhouse gas and carbon footprint data
– create better methodology for parsing data and pulling reports
• Continue working with other departments to gather energy-related data
– write new queries for existing databases to harness full power of available data
– add new scorecard metrics for internal use and analysis
– discuss possible energy-efficiency projects and budget impacts

17. QUESTIONS?
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CTA’s Sustainability Work
www.transitchicago.com/goinggreen
Sonya Dekhtyar
sdekhtyar@transitchicago.com