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

2

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


WHAT IS BIG DATA?

Any dataset that’s too big to fit in a spreadsheet…

And preferably more than one…

3


SMART METERS ARE ENERGY’S MOST EXCITING BIG DATA SOURCE

4

~60Mnationwide

4M+in Illinois


HOW FIRSTFUEL WORKS WITH BIG DATA IN ENERGY

5

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


HOW UTILITIES ARE USING BIG DATA

6

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


BENEFITS OF BIG DATA ANALYTICS

Optimizing efficiency program delivery

Improving customer experience

Increasing efficiency of infrastructure

7

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

8C O N F I D E N T I A L 8C O N F I D E N T I A L 8

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?


BIG DATA CAN ADDRESS ENERGY EFFICIENCY’S BIG CHALLENGES

9

Identify Opportunities

Promote Participation

Measure Real Savings

IDENTIFY EFFICIENCY OPPORTUNITIES

1


METER DATA REVEALS EFFICIENCY POTENTIAL AT PORTFOLIO SCALE

11

EE Program Managers

“Show me all my

customers who need a

lighting retrofit.”


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

2


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


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

3


WHY THE INTEREST IN DATA ANALYTICS-BASED M&V?

• 1-2 year lag in impact evaluation results post-program year is inefficient and costly


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

1

2


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


WHAT STATE POLICYMAKERS CAN DO

21

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

1

2

3

4


No state has achieved 1% savings without an Energy Efficiency Standard.

EFFICIENCY PORTFOLIO STANDARDS PROMOTE ENERGY SAVINGS

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1


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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2


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

24


LEVERAGE METER DATA FOR M&V OR TO PLAN FOR CPP

25

Retrofit

Operational

3


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

26

4


REVIEW: BENEFITS OF BIG DATA ANALYTICS

Optimizing efficiency program delivery

Improving customer experience

Increasing efficiency of infrastructure

27

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

28

THANK YOU

28

Brian BowenRegulatory Affairs [email protected]

http://bit.ly/FFengage

Download our whitepaper,

The Future of Business

Customer Engagement

To Learn More:

APPENDIX


Cost of Eff ic iency in Midwest States 4-Year Average Cost of Saved Energy, $/kWh

4

$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

30


Lifetime Cost of Electricity Resources Nationwide cost ranges for selected new resources, $/MWh

2

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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big data: energy efficiency’s next wave

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