BIG DATA: ENERGY EFFICIENCY’S NEXT WAVE
Transcript of BIG DATA: ENERGY EFFICIENCY’S NEXT WAVE
BIG DATA: ENERGY EFFICIENCY’S
NEXT WAVE
Brian Bowen, FirstFuel Software
NCSL Energy Supply Task Force
August 7, 2016
C O N F I D E N T I A LC O N F I D E N T I A L
AGENDA
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Objective:
Discuss big data in energy efficiency and how policymakers can leverage
data analytics to promote energy efficiency and customer engagement.
• Introduce big data analytics
• Address challenges with energy efficiency
• Present state policy solutions
C O N F I D E N T I A LC O N F I D E N T I A L
WHAT IS BIG DATA?
Any dataset that’s too big to fit in a spreadsheet…
And preferably more than one…
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C O N F I D E N T I A LC O N F I D E N T I A L
SMART METERS ARE ENERGY’S MOST EXCITING BIG DATA SOURCE
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~60Mnationwide
4M+in Illinois
C O N F I D E N T I A LC O N F I D E N T I A L
HOW FIRSTFUEL WORKS WITH BIG DATA IN ENERGY
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FirstFuel harvests
the insights within
energy meter data
Delivering
customer-specific
intelligence
To save energy and
engage business
customers at scale
55MSecure
reads/day
30+ Utility/Retail
customers
N. AMERICA +
EUROPE
3MBusiness Customer
Meters
C O N F I D E N T I A LC O N F I D E N T I A L
HOW UTILITIES ARE USING BIG DATA
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EE Program Managers Customer Engagement Grid Ops & Planning
“How can I keep the
lights on with a
limited budget?”
“How can I help this
customer manage
their bill?”
“Show me all my
customers who need a
lighting retrofit.”
• Estimate savings
potential across
accounts
• Customer seg-
mentation by
multiple variables
• Answer high bill
calls and address
customer complaints
• Understand rate
impacts (TOU or
demand) on bills
• Inform savings
scenarios/M&V for
energy efficiency
• Manage capacity
constraints at the
substation level
C O N F I D E N T I A LC O N F I D E N T I A L
BENEFITS OF BIG DATA ANALYTICS
Optimizing efficiency program delivery
Improving customer experience
Increasing efficiency of infrastructure
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DSM program
planning
Program
participation
Scale analysis
and auditsMarket activity
Baselining and
M&V
Customer
satisfactionCustomer choice
Value from AMI Bytes, not wires
Rate & tariff
education
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ENERGY EFFICIENCY HAS ADVANTAGES AND CHALLENGES
Advantages
• Cheap
• Abundant
• Customers (i.e. voters) like it
Challenges
• Requires customer action
• Requires policy/business model change
• M&V: Are the savings really there?
C O N F I D E N T I A LC O N F I D E N T I A L
BIG DATA CAN ADDRESS ENERGY EFFICIENCY’S BIG CHALLENGES
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Identify Opportunities
Promote Participation
Measure Real Savings
IDENTIFY EFFICIENCY OPPORTUNITIES
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C O N F I D E N T I A LC O N F I D E N T I A L
METER DATA REVEALS EFFICIENCY POTENTIAL AT PORTFOLIO SCALE
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EE Program Managers
“Show me all my
customers who need a
lighting retrofit.”
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0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
Office
Office/Datacenter
Courthouse
Courthouse
Office
Office/Courthouse
Office/Courthouse
Courthouse
Office
Office
Courthouse
Office/Datacenter
Courthouse
Office
Courthouse
Office
Courthouse
Office
Offices/Convention
Courthouse
Office
Office
Courthouse
Offices/Lab
72%Total savings
potential
in top 7 buildings
PRIORITIZING EFFICIENCY POTENTIAL
• Portfolio-wide potential
• Reveal missed opportunities
• Improve ratepayer parity
• Reduce “windshield time”
PROMOTE PARTICIPATION
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UTILITY CUSTOMERS EXPECT A GREAT ONLINE EXPERIENCE
“In today’s world, our customers are having
advanced customer service experiences
with many other companies – they can
order almost anything online and have it
delivered practically the next day, they can
receive call backs when someone is ready
to assist them...and they can text their order
to a restaurant before they even arrive.
They’re expecting the same level of service
from their utility.”
- Mike Innocenzo, COO, PECO, 2014
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DESIGNING FOR DATA-DRIVEN CUSTOMER ACTION
Clear
Personalized
Useful
Designed to be interactive
and engaging
Customer-specific insights
with relevant comparisons
Direct calls to action I’ll do this
SUPPORT MEASUREMENT& VERIFICATION OF SAVINGS
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C O N F I D E N T I A LC O N F I D E N T I A L
WHY THE INTEREST IN DATA ANALYTICS-BASED M&V?
• 1-2 year lag in impact evaluation results post-program year is inefficient and costly
C O N F I D E N T I A LC O N F I D E N T I A L
MEASURING SAVINGS THROUGH DATA ANALYTICS
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Create building models to detect usage changes that can be attributed to EE
Do this for every building that participated in a program or installed a measure
Effect of recorded changes on building-level kWh consumption in percent impacts
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C O N F I D E N T I A LC O N F I D E N T I A L
CALCULATE SAVINGS AT THE GRID LEVEL
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Substation-Level Impacts Service City Impacts
Data analytics can speed up M&V and better inform grid ops.
Aggregate building-level effects to the relevant grid level3
STATE POLICIES TO DRIVE THE NEXT WAVE OF ENERGY EFFICIENCY
C O N F I D E N T I A LC O N F I D E N T I A L
WHAT STATE POLICYMAKERS CAN DO
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Make operations and behavior count:
Retrofit savings are not the only source of energy
efficiency potential
Set strong energy efficiency standards:
EE Portfolio Standards (EEPS) are most effective way
of driving savings
Lead By Example:
State facilities can benefit from a renewed focus on
energy efficiency
Leverage meter data infrastructure:
Meter data can assist with M&V or Clean Power Plan
planning
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C O N F I D E N T I A LC O N F I D E N T I A L
No state has achieved 1% savings without an Energy Efficiency Standard.
EFFICIENCY PORTFOLIO STANDARDS PROMOTE ENERGY SAVINGS
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C O N F I D E N T I A LC O N F I D E N T I A L
MAKE OPERATIONAL AND BEHAVIORAL SAVINGS COUNT
• New California law – AB 802
• Revises CA’s energy benchmarking law
• Dramatic change for several utility energy efficiency programs • Can count savings based on meter data-based
building performance• Count all savings: operational, behavioral and retro-
commissioning activities• Baseline based on existing conditions (rather than
“at code” for California buildings)
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C O N F I D E N T I A LC O N F I D E N T I A L
NEW CA LAW (AB 802): GO AFTER ALL POTENTIAL SAVINGS
Aggregate Potential
Energy Savings
26.4% of potential
savings were in
operational
improvements
49.2% of potential
savings were in
bringing buildings up
to current code levels
24.4% of potential
savings were
above current code
More
than
75% of
potential
savings
Above-code
savings
To-code
savings
Operational
savings
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C O N F I D E N T I A LC O N F I D E N T I A L
LEVERAGE METER DATA FOR M&V OR TO PLAN FOR CPP
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Retrofit
Operational
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LEAD BY EXAMPLE: DEPARTMENT OF DEFENSELeveraging Analytics For Base Energy Efficiency
“FirstFuel used [remote building analytics] to analyze more than 100
DoD buildings, consisting of five different building types, on eleven
military installations in multiple climate zones. To validate the
analytics, an independent building auditor conducted ASHRAE Level
2 onsite audits of 16 of the buildings.
Remote analytics revealed 16-37% more Energy
Conservation Measure savings than the onsite
audits.”
Source: https://www.serdp-estcp.org/News-and-Events/Blog/Expediting-Building-Energy-Audits
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C O N F I D E N T I A LC O N F I D E N T I A L
REVIEW: BENEFITS OF BIG DATA ANALYTICS
Optimizing efficiency program delivery
Improving customer experience
Increasing efficiency of infrastructure
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DSM program
planning
Program
participation
Scale analysis
and auditsMarket activity
Baselining and
M&V
Customer
satisfactionCustomer choice
Value from AMI Bytes, not wires
Rate & tariff
education
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THANK YOU
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Brian BowenRegulatory Affairs [email protected]
http://bit.ly/FFengage
Download our whitepaper,
The Future of Business
Customer Engagement
To Learn More:
APPENDIX
C O N F I D E N T I A LC O N F I D E N T I A L
Cost of Eff ic iency in Midwest States 4-Year Average Cost of Saved Energy, $/kWh
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$0.019
$0.019
$0.017
$0.026
$0.019
Illinois
Iowa
Michigan
Minnesota
Wisconsin Natio
nal A
vera
ge
: $0.0
28/k
Wh
Note: These differ from LBNL average COSE due to methodological differences between the two studies. Source: ACEEE 2014
ENERGY EFFICIENCY IS RELATIVELY INEXPENSIVE IN THE MIDWEST
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C O N F I D E N T I A LC O N F I D E N T I A L
Lifetime Cost of Electricity Resources Nationwide cost ranges for selected new resources, $/MWh
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50
87 95
145
204
230
0
61 45
65
149
197
14 0
50
100
150
200
250
En
erg
yEf
fici
en
cy
G
asC
om
bin
ed C
ycle
Win
d
Co
al
Ro
oft
op
Sola
r
G
asP
eaki
ng
Average Midwest Cost of Saved Energy Source: LBNL 2014, Lazard 2013
AND IT IS STILL THE LOWEST COST RESOURCE OVERALL
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