Phil Farese Creating an Energy Efficient Future

Creating an Energy Efficient Future through Data‐Driven Decision

Making

October 28th, 2013Philip C. Farese; Advantix Systems

Before I begin I would like to recognize those that contributed to this work

Special thanks to:

Roland Risser, Director, Building Technologies OfficeAlexis Abramson, Associate Professor of Mechanical and Aerospace

Engineering, Case Western Reserve UniversityOmar Abdelaziz, Oak Ridge National Laboratory Rachel Gelman, National Renewable Energy LaboratoryBob Hendron, National Renewable Energy LaboratoryEduard Barber, Navigant Consulting (and his team)

Today’s Seminar will address four topics

1. How can we efficiently use energy in the future?

2. What data drives us to this conclusion?3. Are we making progress? LED lighting example

4. What will it take to get there? Liquid desiccant example

How I got into this business

‐ 4 ‐

Focusing on currently commercialized technologies reveals 26%…

Unlocking Efficiency in the U.S. Economy, Granade et. Al; McKinsey & Company

… to nearly 30% savings

22% 29%Real Prospects for Energy Efficiency in the United States, National Academies Press

But identifying the potential isn’t enough to capture it…


8

Behavioral 40% discount factor

Limited understanding of energy use and potential

Source: McKinsey analysis

Solution strategiesManifestation of barrier Potential approach

Home labeling and assessments

Barriers

Structural

Agency issues

Transaction barriers

Pricing distortions

Ownership transfer issues

Behavioral

Risk and uncertainty*

Awarenessand information

Custom and habit

Elevated hurdle rate

Availability

Adverse bundling

Capital constraints

Product availability

Installationand use

Information flow

Educate users on energy consumption

Promote voluntary standards/labeling

Establish pricing signals

Improper installation and use of measures

Limited availability of contractors

Competing uses for a constrained budget

Limits payback to time owner lives in home

Landlord‐tenant issues

Research, procurement and preparation time

Capital outlay

Increase availability of financing vehicles

Provide incentivesand grants

Raise mandatory codes + standards

Support 3rd‐partyinstallation

Innovative financing vehicles

Tax and other incentives

Required upgrades at point of sale/rent

Develop certified contractor market

…capturing it requires comprehensively addressing all adoption barriers


Additional savings, as high as 50‐80% is available if considering newer tech…

Business as usual: based on Energy Information Agency’s Annual Energy Outlook

Currently available technologies (30%): describes technologies as available at the time of the study; represents conservative assumption

Emerging technologies (55%): reflects technology advances “expected” to become commercial and affordable over the course of the study period

Emerging technologies (80%): reflects the best technology “on the bench” regardless of cost or time to commercialization

Nature 488, 275–277 (15 August 2012)

…but deciding which technologies to pursue is not a trivial task…

Potential energy savings of each technology

Cost of tha

t savings expressed

in $/M

MBT

U

Features to note:• Size of “bubble” indicates maturity of technology

•Grey bars indicate competing cost of energy

• Log‐log scale on both axis: vast range of benefits

Cost effective point determined by fuel cost (~$5‐$25/MMBTU)

$(10.00)

$‐

$10.00

$20.00

$30.00

$40.00

$50.00

‐ 5,000 10,000 15,000 20,000 25,000 30,000 35,000

Cost of con

served

ene

rgy, $/M

MBTU Prim

ary

averaged

2010‐2100

assum

inG 1005 de

ployment

Energy Savings, Primary TBTUs/year

Efficiency supply curve, select measures

Measures with lower initial cost

…a resource supply curve can provide a “minimal‐cost path” to savings

55%

80%





We use energy to seize services it enablesNo commonly accepted definition for energy efficiency exists. For the purposes of identifying opportunities to improve the annual energy use associated with buildings on a macroeconomic scale let’s define for each “service” delivered:`

Service Demand per unit stock (e.g., BTUs of heating required to maintain occupant comfort/ home/ year, lumens/ sq. ft. /year)

Efficiency (or more formally intensity): the energy required to meet the service demand (e.g., AFUE, lumens/watt)

Equipment Stock (e.g., number of homes, total square footage)

Total energy use (e.g., trillion BTUs primary energy per year in 2030)

Q

Example: commercial lighting

Illumination (measured in “lux”, lumens/sq. m or lumens/sq.ft.) times number of hours illumination active (not needed)

Lumens x hoursSq. ft.(in 2030)

Lumens per Watt:Incandescent: ~15‐20 Linear Fluorescent: ~80‐100Compact fluorescent: ~ 50‐60 LED: 90 (today) ‐250 (2020)

WattLumens

Total commercial square footage (in market under consideration)

Sq. ft

Total energy use in that year: Trillion BTUs of natural gas in 2030

Watt‐ hours(or kWh)in 2030

Q

The “Prioritization Tool” provides objective analytic input to decision making

• The Prioritization Tool aims to provide an objective comparison of new and existing opportunities for efficiency investment. It represents the 55/5 (even less than 80/20) solution: “55% solution in 5% of the time”

• It estimates potential savings and costs of efficiency opportunities using widely accepted methodology and sources of energy consumption and potential savings

• This enables a “level playing field” comparison of measures• Allows simple and transparent analysis including individual opportunity assessment, portfolio design, and benefits estimation

‐ 200 400 600 800

1,000 1,200 1,400 1,600 1,800 2,000

2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Consum

ption, prim

ary en

ergy (T

Btu's)

Research and demonstrate R‐10 windows in homes

BAU Technical Economic Fit BAU

Applying its most basic form allows us to evaluate the “best case” for a measure

Technology becomes available, with R&D

Stock & Flow deploys technology to 100% of the applicable market for economic calculation (allowing acceleration if applicable)

Stock growth and repurchases continue

BAU includes improvement of inefficient stock and base‐level adoption of efficient stock based on fit

Technical potential deploys efficient technology to 100% of the market overnight regardless of cost

This is energy efficiency!

Economic potential requires tracking the stock through its lifecycleWe use a simplified approach counting only two options (efficient and inefficient) and three points in the “life cycle” (new, existing, and replacement) to realize nine “families” of products to track

Cost of conserved energy represents the other crucial value to calculate

Cash outlays Energy Discount factor

Why I love cost of conserved energy

1. It doesn’t depend on energy prices (you compare it to energy prices)2. Converts to cost/benefit ratio easily (divide energy price by CCE)3. Converts to payback time (divide lifetime by cost/benefit ration)

Developing a scenario from this approach requires accounting for measure overlap

Ventilation

Resid

ential

Commercia

l

Pre‐20

10

New

Heating/ co

oling

distrib

ution

Outdo

or air delivery

Circulation pu

mps

Constant air volume

Variable air volum

e

PC's, Monitors, and TV's

Resid

ential

Commercia

l

Pre‐20

10

New

Desktops

Laptop

s

Displays (m

onito

rs)

TVs

Standb

y

Refrigeration

Resid

ential

Commercia

lPre‐20

10New

Superm

arkets

Not su

per m

arkets

Primary, fu

ll size

, with

Primary, fu

ll size

, no

Primary, co

mpact;

Second

ary; Reach‐

Superm

kt_com

Superm

kt_con

Superm

kt_d

isTop loading freezer;

Fron

t loading

freezer;

Walk‐In_refr

Food

prep & se

rvice

Washing and hot water

Resid

ential

Commercia

l

Electric

Gas

Distilate

Dishwasher, machine

Clothesw

asher,

machine

Clothes d

ryer

Dishwasher, ho

t water

Clothes w

asher #

1, hot

water

Clothes w

asher #

2, hot

water

Non

‐machine

hot

water

Cooking

Resid

ential

Commercia

l

Electric

Gas

Microw

ave

Stove/oven

Range

Broiler

Fryer

Griddle

Steamer

Food

prep.

Lighting

Resid

ential

Commercia

l

Commercia

l

Outdo

or

Incand

escent

Fluo

rescent

HID

Misc.

V. low CRI

Low CRI

Med. CRI

High

CRI

Space conditioning sub‐vector

Resid

ential

Commercia

l

Heating

Cooling

Pre‐20

10

New

Attic

Walls

Basement

Infiltration

Doors

Windo

w‐C

Windo

w‐R

Internal

Czon

e 1

Czon

e 2

Czon

e 3

Czon

e 4

Czon

e 5

ASHP

GSHP

Furnace/RTU

Boiler/ ce

nt. chiller

Sec h

eatin

gA /C ‐ resi

Electric rad;

Wall /w

indo

w A/C

Other (recip, screw

, scroll )

2 x 2 x2 x8 x5 x7=2,240

2x2x8=32

4x4x4=64(16 used)

2x2x5=20

2x 2x2x11=88(64 used)

2x2x2x3=24(23 used)

2x 3x7=42

Taking the inner product of micro‐segment

vectors from two measures yields their overlap

This approach gives significant advantages but requires caution

Advantages:1.Provides transparent and straightforward approach to avoid double counting savings

2.Divides ~40 quads/year of energy use into nearly 2,500 “micro‐segments” averaging under 20 TBTUs/year each

3.Allows opportunity to debug market size, average uncertainty 5.4%

Cautions:1. Includes only first‐order market overlaps • Same‐enduse interactions only • E.g., no lighting impact on space heating or cooling

2. Assumes national average represents shares in each micro‐segment • E.g., cooling in climate zone 1 is the same share of use in new and existing buildings

• However, heating/cooling/ residential/commercial includes a 4‐column matrix for other characteristics

Interactions can take one of two forms

Null

CFL’s reduced the savingsand market LEDs can address

Market: 0.25 quadsSavings: 60%

Savings: 0.15 quads

CFL’s reduce the market, but not the savings (%)Market: 0.25 quads

Savings: 25%Savings: 0.06 quads

LEDs reduced the marketand eliminate CFL savings


Savings 0 quads

Null

LED’s reduce the market, but not the savings (%)Market: 0.10 quads

Savings: 25%Savings: 0.025 Q

Occupancy reduces the market, but not the savings


Savings: 0.56 quads

Occupancy reduced the market not the savings can

address with LEDsMarket: 0.75 quads

Savings: (0.65 Q) 90%

Null

CFL1 quad use75% savings0.75 Q saved

LEDs1 quad use90% savings0.90 Q saved

Occupancy1 quad use25% saved0.25 Q saved

If you do

this m

easure first…

Daylighting reduces the market, but not the savings


Savings : 0.45 quads

Occupancy reduced the market but not the savings

for LEDMarket: 0.75 quads

Savings: (0.65 Q) 90%

Daylighting eliminates the market and savings for

occupancyMarket: 0.60 quads

Savings: 0%

Daylighting1 quad use40% saved0.40 Q saved

CFL (1Q use,75%)0.75 Q saved

LEDs (1 Q use, 90%)0.9 Q saved

Occupancy (1 Q use, 25%)0.25 Q saved

…It has impact on the measure you stage second (Not cumulative!)

Daylighting (40%)0.40 Q savedCFL’s reduce the market, but not the savings (%)Market: 0.25 quads

Savings: 40%Savings: 0.10 quads

LED’s reduce the market, but not the savings (%)Market: 0.10 quads

Savings: 40%Savings 0.04

Occupancy reduces the market and savings for

daylightingSavings: 0.15quads

Savings: 20%

Null

Key: Opportunity is “market reducing, savings preserving”

Opportunity is market and savings reducing

A then B:0.75+0.15=0.90

B then A:0.90+0.00=0.90

A then B:0.75+0.06=0.81

B then A:0.25+0.56=0.81

Building a full scenario requires analyzing all interactions

Five classes of component emerge:A. Cross‐cuttingB. HVAC loadsC. Lighting elementsD.HVAC equipmentE. Other

A

B

C

D

E

Four flags describe “type”:• 0: No interaction• 1: Market reducing, savings (%) preserving

• 2: Market and savings reducing

• NA: Not analyzed

In conclusion, this provides a powerful tool applied to a data repository

Status:• Over 600 measures analyzed• Many scenarios considered, only some of which we discussed, variations explored to date include:• Expanding or limiting the technology set• Incorporating adoption dynamics and the impact of government support

• Varying the price of energy• Exploring the impact of a carbon price

• You can contribute, too. See the RFI at:https://eere‐exchange.energy.gov/Default.aspx?Search=prioritization%20tool&SearchType=#FoaIdc83baeea‐4a16‐48fa‐a123‐7c03796b503b

A final word of caution: optimization is a challenging problem!

$‐

$1

$2

$3

$4

$5

$6

0 5 10 15 20 25 30

It's complicated

Measure 1, then 2Measure 2, then 1Measure 2Measure 1

Staging can’t find minimum cost path to all energy savings levelsIt minimizes cost and calculates savings available at a specified energy cost





SSL Multi‐Year Program Plan, March 2011, http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_mypp2011_web.pdf

Annually the DOE’s Office of Building Technology develops a lighting efficiency forecast…

$‐

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

$14.00

LED PackageCost, 2010

EpitaxialGrowth Toolsand Processing

LED ChipManufacturing

Automated LEDPackaging

Expectedmanufacturercontribution

LED Packagetarget cost,

2015

Cost: $/kLm

Example: LED package cost reductions from Manufacturing InitiativePackage Wafer process Substrate Epitaxy Phosphor Reductions

Spending: $XXXX $ XXXX $ XXXX $ XXXX

…This document, prepared with industry input, helps determine research programs DOE should fund…

DOE and its SSL partners have developed a multi‐year R&D plan to ensure that DOE is funding the appropriate R&D topics to successfully move SSL from lab to market. The R&D roadmap is updated annually with input from industry partners and workshop attendees, and guides the development of annual SSL R&D solicitations.

Source: Multi‐year project plan, March, 2010; SSL Manufacturing Roadmap July, 2010; TDM

“Normal” bulb to

cost ~$7‐8

…and set the SSL goal, to “capture 70% energy savings due to lighting improvements by 2020”

DOE and its SSL partners have developed a multi‐year R&D plan to ensure that DOE is funding the appropriate R&D topics to successfully move SSL from lab to market. The R&D roadmap is updated annually with input from industry partners and workshop attendees, and guides the development of annual SSL R&D solicitations.

Source: Multi‐year project plan, March, 2010; SSL Manufacturing Roadmap July, 2010; TDM

Example: LED Luminaire efficiency improvements and cost reductions

‐2.0

‐1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

2010 Efficiencyimprovements

Lampsavings

Packagesavings

Chipsavings

2020 cost CFLCapital

Net 2020cost

Cost of con

served

ene

rgy, cen

ts/kWh

EfficiencyLampPackageChip

Actual prices:

~$50

$16.65

265

200

Actual performance:

Q: So how are we doing as a nation?



A: Actually tracking or exceeding these targets!

2010 2015

L‐Prize:94 Lm/Watt$50.00/bulb

Philips TLED:200 Lm/WattNo pricing

Cree:89 Lm/Watt$13.30/bulb

Bulb Watts Capital cost ($/bulb)

Lifetime (hours)

kWh/ year

Total cost/year

CCE ($/kWh)

Annual dividend

Incandescent 60 0.43$ 1000 43.8 5.13$ LED goal, 2020 4 4.00$ 30000 2.92 0.42$ (0.005)$ 126%LED goal, 2015 6 8.00$ 30000 4.38 0.68$ (0.003)$ 57%CREE Soft White 60 W 9 13.30$ 25000 6.57 1.11$ 0.002$ 32%ENERGY STAR CFL 15 2.49$ 10000 10.95 1.39$ (0.004)$ 175%Lprize LED 10 50.00$ 30000 7.3 2.02$ 0.025$ 8.1%Halogen incandescent 43 1.75$ 1000 31.39 4.73$ 0.078$ 103%Cost sources: Home depot & 1000bubls.com from 3/8/2012, 1027/2013

So what does this mean for you and me?

The best investment, offering a 175% ROI over the incandescent bulb, is still the CFL

But these have many market barriers and have quite the bed reputation at this point…

The CREE bulb is actually the loeset total annual cost available today (but not the best investment)

It is very likely that soon the lowest energy user will be the best investment and the lowest cost light bulb… beginning a revolution





Excessive humidity causes a multitude of problems

1. Discomfort 2. Mold and mildew growth: health problems

3. Damage to building materials 4. Process requirements

60

60

Injection moldingCold

storage

Source and copyright pending: Advantix Systems

Traditional “design” conditions do not reflect the true challenge of moisture control in modern buildings

Source: TIAX

30 Years Observed Outdoor Air Conditions, Cairns AFB Alabama

Moisture Co

nten

t

Temperature

30 Years Observed Outdoor Air Conditions Cairns AFB Alabama

Humidity design

condition

Part load condition

Cooling design

condition

Realistically, the worst‐case conditions are already at about 50% ‐ smart designers are increasingly moving away from the “cooling design” load

34

0%

20%

40%

60%

80%

100%

1980 1985 1990 1995 2000 2005

Percen

t Moisture Load

(SHR)

Shoulder/Part Load Design Conditions

Albuquerque Boston Atlanta Miami

0%

20%

40%

60%

80%

100%

1980 1985 1990 1995 2000 2005

Percen

t Moisture Load

(SHR)

Dehumidification Design Conditions

Albuquerque Boston Atlanta Miami


Approach Technology Manufacturers Drawbacks

Mechanical Dehumidification/DOAS

• Overcools as above, has packaged hot gas reheat

• Specialized coils to allow greater moisture removal

• Aaon• Addison• Desertair• Dectron• Poolpak

• Expensive• Energy intensive• High maintenance requirements

Solid Desiccant • Hygroscopic chemistry adsorbs moisture

• Heat addition necessitates pre‐coolingand/or post‐cooling of air

• Bry‐air• Munters

• Expensive• Energy intensive• High maintenance requirements

• Often requires pre‐cooling and post cooling equipment

Liquid Desiccant • Hygroscopic chemistry adsorbs moisture

• Cools and dries air simultaneously

• Advantix Systems• Kathabar• 7AC Technologies

• Cannot reach extremely low dewpoints (< 10 gr/lb)

There are 3 basic approaches to humidity control

35Source and copyright pending: Advantix Systems

36

Approach 1: Mechanical refrigeration + reheat

Example 1: bringing 3000 CFM of humid outdoor‐air to room‐neutral

217 MBH(18 tons)

74 MBH*

Total: 291 MBH

* Can be condenser heat in some cases

4. Shoulder monthsare even worse

2. Needed work

3. Wasted work

1. Target enthalpy

Adding reheat enables humidity control with mechanical refrigeration, but at high energy cost


Limited technical headroom exists to improve vapor‐compression efficiency

‐ 37 ‐

Room A/CEER ~6

Central A/C~11 SEER available

Central A/C15 SEER availalbe

Today best GSHP provides ~24+ SEER cooling

"Perfect" transfer from 55F

evaporator to 80F condenser?

0%

20%

40%

60%

80%

100%

1980 1990 2000 2010 2020 2030 2040

75% red

uctio

n accomplish

ed

15% (at m

ost) to

go

Many manufacturers are looking for new

directions


A desiccant wheel can do the job but also requires significant excess energy input

38

Method 2: Solid desiccant

Example 1: bringing 3000 CFM of humid outdoor‐air to room‐neutral

Total: 445 MBH

Note: Pre + Post cool configuration (not shown) requires similar energy

input

* A portion, not all, can be condenser heat

252 MBH*(For regeneration)

193 MBH(16 tons)

2. Needed work

3. Wasted work

1. Target enthalpy


Liquid desiccant can cool and dry air simultaneously, without reheating, and regenerates with less energy


40

Advantix equipment integrates a heat pump thereby using waste heat to regenerate its desiccant


The psychometric chart can show fully what is happening

Refrigerant to air (pre‐cool coil)

Media bed

Media bed

Post‐treat options

Regeneration pre‐heat coil

Liquid Hx

Process side

Liquid Hx

Regenside

Comp.

Moisture transport by osmosis

1a

1d

1a

1d 2a

2d3d

3a3a

3d3d

3a

4a 2a

4a4a

4d4d

2d

4d


42

We need data to test our equipment: Big Box pilotKey

Run “Lennox only”

Run in both modes

Advantix units

Excluded from pilot

Pilot design

• Alternate operating “modes”:oThree weeks of baseline data with 13 Lennox units

oThree weeks of test data with 6 Advantix and 7 Lennox units

• Conditions in store kept at equivalent comfort levels

• Energy use and performance of all units monitored

60

65

70

75

80

85

90

95

100

74 75 76 77 78 79 80 81 82

Humidity

Ratio (g

rains/lb)

Temperature (Fahrenheit)

Day time indoor conditions when outdoor conditions meet design specification

HR, Advantix

HR, Lennox

Comfort line

75.6, 76.978.8, 77.2

43

Theory of design: control humidity to maintain comfort

Comfortable conditions are defined as meeting OSHA (60% RH) and ASHRAE 55 and requirements: Humidity below 60% and below humidity level given typical summer clothing, and metabolic activity of shopping/ walking

Conventional uncomfortable

22%

Advantix uncomfortable

5%Conv.

Temperature (increasing to right)

Absolute hum

idity

(increasing up

wards)

Conven‐tional

Advantix

Not comfortable

Comfortable

44

We directly monitored relevant variables

Each RTU & RDU provides:• Return temperature and humidity ratio• Supply temperature and humidity ratio• Power (directly calculated from voltage, three‐phase power, and power factor measurements)

Additionally we monitor:• Six indoor air temperature and humidity sensors

• Six outdoor air temperature and humidity sensors

Directly measures energy use and performance of each RTU/RDU in both modes of operation.

Allow us to correct baseline to account for weather changes; also allows us to build predictive model (discussed below)

Additional factors may be important:• Store occupancy (ticket counts may be used as a proxy)

• Schedule changes (e.g., stocking hours; none anticipated currently)

Would allow us to account for any major impacts to energy consumption outside of expectations

45

The final cycle shows 11.2% energy savings

0

50

100

150

200

250

300

6/12/2013 0:00 7/3/2013 0:00 7/24/2013 0:00

Power draw (kW) in 5‐minute sample

Uncorrected energy use shows 11.2% savings

Advantix and LennoxLennox onlyTransition

Average:151kW

Average:170 kW 11.2%

savings

This initial analysis is promising but makes no corrections for weather, operational, or other differences and

46

We built two models: one for each mode of operation…1. Measure power use in both “modes” of operation

2. Measure independent variables required to interpolate data:• Outdoor temperature• Outdoor humidity• Indoor set points• Internal loads• Occupancy

3. Build two models, one for each mode, that fit the measured energy use profile

4. Compare “interleaved” model and data for both modes to determine energy savings

Energy use (kWh)

Time (days)

Lennox only data

Lennox only model

Advantix mode data

Advantix mode model

47

…Our principal analysis uses “day‐pairs” to estimate energy use

We identify days with similar temperature and humidity profiles:• Excellent correction for weather• Avoids “trap” of thinking that average conditions mean comparable energy use• Remains limited in difference in building dynamics, customer activity, etc.

0

50

100

150

200

250

300

60

80

100

120

140

160

180

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Power (kW)

Tempe

rature (B

lue) and

Hum

idity

ratio (green

)

Advantix 06/09: 86.2, 140.9; Lennox 06/18: 89.5, 139.5; Savings: 32.2%

Advantix temperatureLennox TemperatureAdvantix HRLennox HRAdvantix energy useLennox energy use

48

With 40+ days in each mode we generate over 1,600 day‐pairs

5/1/20

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8/6/20

13

4/24/2013 35.3 35.1 35.3 29.0 22.6 24.0 28.4 18.6 22.8 29.0 38.8 45.1 36.0 39.5 21.4 14.6 16.3 26.1 35.9 55.6 50.8 49.6 48.7 62.4 64.5 57.1 55.9 52.4 46.9 48.4 47.3 45.0 52.7 50.4 54.3 50.2 52.1 63.5 66.2 59.1 51.0 50.4 53.2 55.2 60.1 55.8 4/25/2013 37.1 37.0 37.8 30.1 23.8 23.6 24.2 15.3 19.2 30.8 41.3 48.9 38.8 42.5 19.9 12.2 14.3 28.8 39.4 58.7 54.3 51.8 51.7 66.0 67.9 60.7 59.4 56.4 50.3 52.5 51.1 49.0 56.5 53.9 58.1 53.6 55.6 67.0 70.4 62.9 54.6 54.4 57.0 59.2 63.1 59.4 4/26/2013 35.1 35.4 35.3 30.5 25.4 27.1 28.0 19.8 21.8 28.1 38.9 46.0 37.6 41.5 22.2 14.8 15.9 26.5 37.0 56.9 52.0 50.2 50.0 63.5 64.9 58.0 56.7 53.1 48.0 49.3 48.4 45.9 53.5 51.2 55.2 51.2 53.6 64.4 66.5 60.5 51.9 51.4 53.5 56.6 61.2 57.6 4/27/2013 32.6 33.1 31.9 27.0 17.0 19.5 31.8 19.3 25.9 35.0 38.1 42.3 33.7 38.6 9.1 17.0 19.4 27.4 33.5 52.8 48.6 47.6 46.9 60.8 62.8 55.0 53.9 50.5 44.2 47.2 45.5 43.5 51.4 47.9 52.3 48.4 50.2 61.8 64.0 56.8 49.3 48.5 50.1 53.8 58.7 54.7 4/28/2013 19.1 20.8 21.4 30.6 37.5 48.1 57.7 46.4 49.1 32.3 28.2 31.8 27.1 30.7 45.0 43.3 41.3 23.0 25.0 40.7 39.1 39.7 36.9 47.2 45.7 43.2 41.9 39.1 36.4 35.0 34.5 31.3 38.5 37.6 39.3 34.9 39.4 49.4 47.8 45.0 37.5 36.7 36.7 41.5 45.9 42.5 5/25/2013 24.6 24.4 26.3 22.7 21.0 31.8 40.2 28.0 30.4 24.0 26.4 34.4 23.6 27.8 28.3 27.8 27.0 15.0 24.9 43.4 37.4 36.6 35.6 49.7 50.2 43.3 41.9 39.8 33.4 34.9 33.8 31.9 38.9 36.8 41.2 37.1 38.8 49.7 53.5 46.0 37.1 37.6 41.2 42.1 47.0 43.2 5/26/2013 20.1 13.8 21.8 28.8 35.8 48.7 59.3 47.1 49.2 29.7 15.3 22.2 17.3 20.3 45.9 45.9 44.9 17.6 15.7 28.4 23.4 25.4 22.0 33.2 33.3 27.5 25.9 23.7 22.5 18.7 19.8 16.6 22.2 22.6 24.8 21.4 24.1 32.9 35.2 29.7 21.9 21.6 25.5 25.8 30.8 27.2 5/27/2013 21.0 15.5 19.0 33.4 40.2 51.8 68.3 54.7 58.6 44.1 21.6 17.2 17.1 24.7 49.2 52.3 51.7 28.6 12.1 24.1 20.5 25.8 19.9 29.4 31.3 24.1 23.5 21.4 17.7 19.3 16.4 14.2 21.9 18.4 21.5 18.3 21.1 32.0 28.3 26.0 20.1 19.7 18.3 24.1 31.5 27.1 5/28/2013 21.1 16.5 18.0 34.9 44.3 55.1 69.6 56.6 60.7 44.5 24.3 22.3 23.6 28.4 52.3 54.2 53.0 31.8 18.4 28.0 27.7 31.5 26.4 34.4 36.3 30.5 29.7 25.4 26.2 25.0 22.6 20.4 27.1 25.9 27.5 23.9 26.5 37.2 33.1 32.2 26.1 24.1 23.4 29.5 36.3 32.2 5/29/2013 22.4 14.0 20.2 33.3 42.0 53.6 68.0 54.7 58.6 41.1 20.1 17.4 18.7 24.1 50.7 52.1 52.2 27.5 13.9 22.0 20.4 25.7 19.6 28.8 30.1 23.7 22.2 19.7 21.0 17.6 17.2 14.9 18.7 19.3 20.2 17.8 19.9 29.3 28.6 24.4 19.2 16.5 17.4 21.9 27.7 23.7 5/30/2013 28.7 19.9 28.2 37.4 44.6 58.0 71.7 58.6 61.2 42.5 20.4 19.1 21.0 25.5 56.4 58.4 57.5 29.1 18.3 18.3 14.5 22.5 16.2 22.6 22.8 15.1 13.8 14.4 18.4 12.4 14.0 12.5 10.0 14.2 13.7 12.5 14.8 20.0 22.3 17.4 13.0 13.3 15.4 14.3 20.3 17.8 5/31/2013 28.8 20.6 28.3 39.2 48.2 60.6 74.7 60.8 64.7 45.9 23.1 20.2 24.3 28.8 57.1 59.6 59.4 33.3 19.9 17.8 19.0 26.5 21.5 25.8 28.1 18.4 17.0 16.0 23.2 18.4 18.2 17.0 15.6 18.9 16.8 16.3 18.0 22.5 21.8 19.5 17.2 16.3 16.2 17.0 21.4 20.5 6/1/2013 29.3 20.3 30.2 35.2 41.3 57.0 69.5 55.2 57.8 40.5 19.4 21.4 18.5 20.8 57.2 59.7 58.1 27.8 19.4 18.4 12.2 19.7 14.2 23.5 26.7 16.6 14.7 14.6 17.6 13.7 14.9 14.1 12.9 15.4 16.1 13.6 12.6 19.4 26.1 17.9 14.0 14.8 19.5 13.4 18.0 14.6 6/2/2013 24.5 16.9 25.0 33.9 41.3 54.6 69.4 55.9 58.7 42.1 19.2 18.1 18.5 23.2 52.7 54.8 54.3 27.0 14.1 20.5 15.8 21.3 15.2 24.3 27.3 18.9 17.9 18.2 17.6 14.9 14.8 13.0 15.5 15.3 16.8 14.9 17.3 25.4 25.2 19.9 15.8 16.0 18.2 18.5 24.5 19.6 6/3/2013 32.4 25.3 30.3 43.7 52.2 64.0 78.2 65.7 68.6 49.1 26.9 21.7 28.9 33.4 62.0 63.5 62.4 37.4 24.5 21.2 21.1 30.1 24.2 27.4 28.2 19.9 18.7 16.5 26.1 20.3 22.4 20.0 16.4 20.6 16.8 18.4 21.4 22.2 20.4 20.4 19.5 18.7 15.4 18.6 21.6 22.1 6/4/2013 39.1 32.7 38.5 51.2 59.4 72.0 86.7 73.3 76.2 56.0 33.1 28.0 35.3 40.3 68.8 71.4 70.3 43.3 30.4 20.7 25.3 34.1 29.3 29.6 29.9 17.8 18.2 20.9 29.7 24.6 25.1 24.4 18.7 23.4 19.4 22.1 25.2 17.7 12.5 19.2 21.8 22.2 18.7 20.5 23.0 26.3 6/5/2013 40.8 32.6 37.3 50.2 58.5 69.3 87.2 72.7 77.8 60.5 36.8 27.2 35.1 41.1 66.7 71.4 71.1 47.0 30.2 18.6 28.3 36.8 31.7 34.5 37.2 23.9 24.4 24.0 31.6 30.1 28.1 28.0 24.3 26.9 23.1 26.7 26.6 25.3 19.2 20.5 26.5 24.0 18.7 24.3 27.9 29.3 6/6/2013 32.8 22.7 28.4 39.9 50.2 62.4 76.4 62.7 67.4 51.4 30.8 24.1 27.5 31.8 58.8 62.9 62.3 39.5 23.9 16.9 25.1 33.5 26.2 32.0 36.3 26.4 26.2 22.6 29.4 27.7 25.9 25.8 23.5 26.2 23.8 24.5 22.8 28.4 27.7 21.8 25.4 23.2 17.8 22.7 26.6 22.6 6/7/2013 42.6 33.5 41.0 50.1 57.0 69.0 82.7 70.4 72.9 50.7 32.1 28.4 35.1 37.2 70.3 70.4 68.9 42.0 32.5 26.6 25.4 35.3 29.7 31.6 34.5 23.8 22.8 18.9 31.5 24.2 27.5 24.9 20.8 26.4 22.4 25.4 26.2 22.2 21.4 23.2 24.4 21.9 23.2 20.2 21.7 24.2 6/8/2013 35.3 28.1 36.2 44.5 50.9 65.7 79.3 64.9 67.5 49.5 25.5 24.5 26.8 31.6 62.8 66.6 65.4 35.5 24.0 16.4 16.6 24.8 20.1 22.5 23.2 9.0 9.4 17.2 19.8 16.7 15.8 17.1 12.2 14.5 13.4 14.9 16.9 14.9 16.7 14.0 12.7 17.1 16.5 16.2 19.0 20.5 6/9/2013 40.6 34.0 39.8 51.3 57.7 70.1 87.4 73.6 76.8 58.3 35.2 28.8 35.4 39.7 69.7 72.9 71.1 45.2 31.0 22.0 25.6 35.3 29.5 28.0 31.4 19.3 20.7 22.8 28.8 26.3 25.0 24.5 21.6 24.1 21.9 22.8 25.5 23.6 15.4 19.7 23.4 23.1 21.1 20.7 27.7 26.4 7/7/2013 29.9 23.4 31.1 39.6 44.9 60.2 74.1 59.7 62.2 44.8 21.2 20.6 21.8 25.9 58.0 61.6 60.1 30.8 18.9 16.8 14.4 22.0 15.1 19.5 21.3 11.5 10.9 15.5 14.6 12.4 10.2 10.9 10.3 11.0 12.0 10.4 12.5 18.9 22.1 15.1 9.7 12.8 17.0 15.6 20.9 18.2 7/8/2013 24.8 18.8 25.8 32.1 35.8 52.4 66.8 52.5 54.8 41.4 17.8 18.9 14.4 19.7 51.3 55.6 53.9 25.8 12.4 20.3 13.3 17.7 10.6 22.8 25.6 15.0 15.1 19.0 9.0 13.7 9.1 10.4 13.8 9.4 15.7 12.9 13.0 23.1 29.2 17.4 12.5 14.8 20.6 18.5 25.3 18.4 7/9/2013 22.8 17.9 23.2 29.2 35.1 48.5 62.7 50.0 52.2 39.0 16.7 19.2 15.0 18.7 48.8 51.6 50.5 25.1 14.6 21.8 14.3 18.4 13.9 26.6 30.4 21.7 20.3 17.8 15.7 14.8 16.5 15.0 17.8 15.7 20.7 18.1 17.2 26.5 31.1 21.9 17.6 16.5 21.5 20.0 26.2 20.3 7/10/2013 31.8 23.6 31.0 41.2 48.9 61.2 76.3 62.9 66.2 47.0 23.7 20.2 25.8 29.9 60.2 62.0 61.0 34.2 21.6 19.3 17.3 26.7 21.1 23.8 26.2 16.7 15.3 13.6 22.2 16.5 17.7 15.4 14.0 17.3 15.7 15.7 17.5 20.2 18.4 17.6 16.2 15.3 16.0 15.0 20.9 18.8 7/11/2013 35.4 27.8 36.4 42.8 48.9 64.9 78.1 64.2 65.9 48.1 24.2 24.7 26.1 29.6 63.3 66.6 64.9 34.1 24.1 18.0 15.1 22.3 17.8 21.6 22.7 9.6 10.0 16.6 18.0 14.8 14.2 15.2 9.7 12.9 12.7 14.2 15.1 13.9 18.5 13.6 12.0 16.2 16.2 16.2 16.2 18.1 7/12/2013 31.6 23.4 29.7 33.5 40.5 55.4 70.1 57.4 59.1 45.6 22.9 21.9 21.0 23.1 56.3 59.2 58.0 31.4 19.3 23.2 16.0 22.7 17.3 27.7 32.1 22.3 21.0 19.6 19.8 19.5 20.1 18.7 17.0 17.9 20.7 21.3 19.1 25.4 30.1 19.8 20.7 18.9 22.7 20.4 24.8 17.4 7/13/2013 34.1 25.4 31.3 42.9 50.7 60.1 76.8 62.4 68.2 50.9 29.8 22.3 28.6 33.8 59.2 61.8 61.9 39.2 25.2 22.1 25.5 33.0 28.7 34.2 36.9 25.8 24.7 20.4 28.8 25.7 25.6 24.0 23.7 25.3 23.1 24.7 24.9 28.4 26.6 24.5 24.7 20.0 20.8 22.4 26.1 26.7 7/14/2013 27.0 18.7 27.9 35.5 42.5 54.8 71.0 55.9 60.5 44.2 22.3 19.7 19.8 25.2 53.3 56.2 56.3 29.4 16.3 18.6 18.3 23.9 19.4 27.3 30.9 19.6 18.7 19.6 19.9 18.6 16.2 16.2 17.9 18.4 19.2 17.2 18.0 25.6 26.3 20.2 17.8 16.6 20.1 18.4 24.7 21.3 7/15/2013 27.5 19.1 28.2 34.2 41.1 56.7 69.3 55.8 57.9 40.1 16.4 21.5 19.2 22.7 55.1 57.5 55.7 26.8 18.0 21.2 14.9 19.9 15.4 25.4 25.7 17.7 16.7 15.5 17.6 14.5 14.9 13.5 14.1 14.5 17.3 14.7 15.8 22.2 26.3 19.6 14.8 15.5 19.2 15.5 23.0 17.6 7/16/2013 24.1 19.6 21.9 35.2 41.8 53.8 69.1 56.3 59.2 44.3 21.1 19.0 20.1 26.2 51.1 53.8 53.0 29.9 16.5 26.1 20.7 26.3 20.9 29.8 30.3 24.1 23.7 21.6 19.0 19.4 18.9 16.2 22.5 18.1 22.2 19.3 23.2 32.0 28.7 26.6 20.7 21.4 18.9 25.4 31.7 27.5 7/17/2013 23.8 23.1 23.2 40.5 48.7 59.2 72.1 58.6 62.9 45.4 25.7 26.6 27.9 34.0 55.0 56.6 55.5 33.1 24.3 32.2 30.5 33.8 31.1 37.5 36.6 31.5 30.4 28.3 28.2 26.3 24.7 22.4 29.3 28.0 29.8 26.6 31.1 39.1 34.8 36.1 27.4 28.4 26.9 33.0 38.2 37.2 7/18/2013 33.6 29.9 31.4 45.9 55.3 63.7 78.5 65.0 70.2 52.7 36.1 33.0 36.9 41.6 61.3 62.9 62.3 41.6 31.8 34.1 37.8 39.4 37.9 43.2 44.8 35.9 35.5 33.6 36.5 34.7 32.7 31.7 34.1 35.2 35.4 33.7 36.1 41.0 35.0 37.8 34.1 31.5 32.4 36.3 42.1 41.7 7/19/2013 42.6 34.2 41.4 51.1 58.5 71.2 84.8 72.4 74.6 53.3 32.8 29.0 35.9 39.1 71.2 71.9 70.7 42.8 32.4 24.8 25.0 34.0 29.3 31.0 32.6 21.1 20.6 19.7 30.9 24.3 27.1 25.2 19.3 25.1 21.1 24.5 26.1 18.4 17.6 21.0 23.6 22.1 22.7 20.4 20.9 24.4 7/20/2013 43.5 36.7 44.4 52.2 58.1 72.9 88.2 73.8 76.5 58.6 34.4 30.8 35.1 39.6 71.1 75.6 74.0 44.5 32.5 19.7 22.6 32.0 27.3 29.5 31.9 15.7 16.5 22.2 27.0 25.0 24.0 25.1 19.0 21.5 19.4 22.2 23.3 14.6 15.2 15.8 20.7 23.2 21.9 20.8 21.9 24.4 7/21/2013 44.2 36.3 43.4 53.0 59.6 73.0 89.2 75.1 78.1 59.0 35.0 29.2 35.9 40.4 72.0 75.4 74.2 45.7 32.5 20.8 23.7 33.7 28.8 29.8 31.8 17.3 17.7 20.3 28.7 25.0 25.5 24.9 18.9 22.7 18.9 23.3 24.6 15.7 12.3 16.0 22.1 22.3 20.2 19.5 20.8 24.2 7/22/2013 35.3 27.2 32.4 38.8 43.5 59.4 73.9 61.5 62.8 49.6 24.4 23.5 23.9 28.2 58.0 63.9 61.9 34.8 22.4 16.2 13.6 21.0 15.8 19.5 22.9 7.8 8.3 17.3 13.9 15.5 12.8 14.5 10.2 10.1 12.0 14.4 13.0 13.6 18.6 11.8 11.7 14.7 15.5 14.2 18.5 17.3 7/23/2013 40.1 31.9 39.3 46.1 51.0 66.3 81.0 66.4 68.9 52.7 30.7 26.8 29.9 30.9 66.8 70.5 68.9 40.4 27.5 16.3 19.3 27.5 20.3 23.5 29.3 16.1 16.0 19.0 21.7 20.0 19.4 21.3 15.3 18.9 17.0 20.0 16.7 17.3 21.3 13.2 17.8 17.0 20.9 16.9 20.7 18.5 7/24/2013 36.2 28.3 36.5 39.6 43.8 59.9 74.7 59.6 62.3 49.2 27.2 25.3 25.3 25.2 61.7 65.7 64.1 36.4 23.6 19.4 17.4 22.5 16.6 23.0 31.1 19.4 18.4 20.9 18.1 19.1 18.5 20.0 17.6 17.4 19.9 20.7 15.6 21.2 29.8 17.0 18.9 15.9 25.9 19.4 22.7 16.1 7/25/2013 33.6 29.2 34.9 35.5 37.0 56.0 67.0 54.6 53.1 42.0 22.2 30.0 21.9 20.4 58.3 61.2 58.3 30.1 25.6 32.9 17.5 17.0 15.6 26.9 31.1 24.9 23.9 25.6 17.1 18.9 21.9 21.9 22.7 19.5 27.2 24.0 20.7 27.8 39.4 27.3 22.4 24.8 32.7 24.7 30.5 20.1

49

Twenty‐six day pairs emerge as fitting well

Using 26 day‐pairs selects the most similar number of days in the two cycles:• Adding day‐pairs decreases the degree of similarity in outdoor conditions• Similarly, adding day‐pairs increases the standard deviation of our savings estimate• Identifies 8 unique days in Advantix and Lennox period, 13 in Lennox only period

0.02.04.06.08.010.012.014.016.018.020.0

1 4 9 16 25 36 49 64 81 100 121 144 169 196 225

OA Weather deviation vs. number of day‐pairs used

50

Average of selected day pairs shows 17.4% savings with a

standard error of 2.3%

Date of Lennox only cycle

Date of Advantix & Lennox cycle

RMS of OA weather

Advantix energy savings Weight

6/18/2013 7/22/2013 7.8 36.7% 0.016546/19/2013 7/22/2013 8.3 31.4% 0.014386/18/2013 6/8/2013 9.0 32.2% 0.012325/14/2013 4/27/2013 9.1 27.4% 0.012086/19/2013 6/8/2013 9.4 26.5% 0.011236/18/2013 7/11/2013 9.6 11.6% 0.010756/25/2013 7/11/2013 9.7 4.2% 0.010708/1/2013 7/7/2013 9.7 16.4% 0.010556/25/2013 5/30/2013 10.0 8.3% 0.010006/19/2013 7/11/2013 10.0 4.3% 0.009946/26/2013 7/22/2013 10.1 23.1% 0.009756/23/2013 7/7/2013 10.2 9.6% 0.009576/25/2013 7/22/2013 10.3 31.4% 0.009436/25/2013 7/7/2013 10.3 22.2% 0.00937



RMS of OA weather

Advantix energy savings Weight

6/24/2013 7/8/2013 10.4 ‐1.6% 0.009296/28/2013 7/7/2013 10.4 2.8% 0.009176/15/2013 7/8/2013 10.6 3.6% 0.008936/24/2013 7/7/2013 10.9 11.4% 0.008496/19/2013 7/7/2013 10.9 22.2% 0.008476/26/2013 7/7/2013 11.0 12.8% 0.008246/18/2013 7/7/2013 11.5 28.2% 0.007628/1/2013 7/22/2013 11.7 26.3% 0.007297/31/2013 7/22/2013 11.8 28.5% 0.007206/27/2013 7/7/2013 12.0 11.3% 0.006958/1/2013 7/11/2013 12.0 ‐2.9% 0.006925/18/2013 5/27/2013 12.1 23.9% 0.00686

Average 17.4%Weighted average: 18.4%Standard deviation: 11.7%

2.3%Standard error:

Focusing on these pairs with well matched weather suggests 17.4% (15.1% ‐ 19.7%) energy savings

51

Nighttime savings is considerably higher supporting expectations that savings increase “off‐season”

Nighttime energy savings approaches 40%!!



RMS of OA weather

"Day": 6AM ‐ 10 PM

"Night": 10 PM ‐ 6 AM

6/18/2013 7/22/2013 7.8 29% 57%6/19/2013 7/22/2013 8.3 26% 49%6/18/2013 6/8/2013 9.0 23% 56%5/14/2013 4/27/2013 9.1 12% 77%6/19/2013 6/8/2013 9.4 20% 47%6/18/2013 7/11/2013 9.6 0% 45%6/25/2013 7/11/2013 9.7 ‐3% 30%8/1/2013 7/7/2013 9.7 9% 39%6/25/2013 5/30/2013 10.0 1% 34%6/19/2013 7/11/2013 10.0 ‐5% 33%6/26/2013 7/22/2013 10.1 24% 21%6/23/2013 7/7/2013 10.2 6% 23%6/25/2013 7/22/2013 10.3 27% 46%6/25/2013 7/7/2013 10.3 17% 38%



RMS of OA weather

"Day": 6AM ‐ 10 PM

"Night": 10 PM ‐ 6 AM

6/24/2013 7/8/2013 10.4 ‐6% 18%6/28/2013 7/7/2013 10.4 5% ‐6%6/15/2013 7/8/2013 10.6 ‐5% 32%6/24/2013 7/7/2013 10.9 9% 20%6/19/2013 7/7/2013 10.9 16% 41%6/26/2013 7/7/2013 11.0 14% 10%6/18/2013 7/7/2013 11.5 20% 51%8/1/2013 7/22/2013 11.7 20% 46%7/31/2013 7/22/2013 11.8 22% 47%6/27/2013 7/7/2013 12.0 7% 28%8/1/2013 7/11/2013 12.0 ‐14% 31%5/18/2013 5/27/2013 12.1 9% 74%

Average 10.5% 37.2%Weighted average: 11.7% 39.2%Standard deviation: 11.5% 18.0%

2.2% 3.5%Standard error:

52

Finally we use an eleven‐parameter model to fit the data for these days and estimate the savings for the full pilot ( 1 of 2)

Parameter Type Rationale

Constant Always fit Required offset term for multi‐linear fit

Difference in temperature

Always fit Weather‐dependent heat load will be proportional to difference indoor and outdoor temperatures

Lag for temperature difference

Fit once Store construction and desired set points introduce heat capacity that slows weather dependent heat transfer

Difference in humidity ratio

Always fit Weather‐dependent moisture load will be proportional to humidity ratio difference

Lag for humidity ratio

Fit once Store construction and desired set points introduce moisture retention

53

Finally we use an eleven‐parameter model to fit the data for these days and estimate the savings for the full pilot ( 2 of 2)

Parameter Type Rationale

Occupancy Always fit Represents load from store occupants

Lag for occupancy

Fit once Air distribution introduces time delay between load introduction and unit response

Lighting Always fit Hour of week lighting impact on HVAC deduced from data (i.e., common mode signal)

Lag for lighting

Fit once Lighting energy heats objects in the store that radiate and conduct heat to provide HVAC load

Door opening

Always fit Clearly on hot and humid days we see sensors near the doors increase well above other units. We use this difference to attempt to correct for the load arising from door infiltration differences

Door lag Fit once Similar to other loads: heat and moisture transport from doors faces a delay before impacting HVAC

54

…Further it allows us to estimate the uncertainly in savings by building a likelihood curve

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

Relative Likelihoo

d

Energy Savings

Relative Likelihod of Energy Savings for Pilot

Most likely value: 19.0%; Risk adjusted value: 17.4%; 90% confidence interval:

11.5% ‐ 22.0%

55

Finally we can use this model to estimate annual energy savings at this, and other, stores

City Miami Houston Atlanta Baltimore Kansas Chicago BostonConventional energy use 1,294,671 1,094,421 893,234 754,468 781,826 668,820 652,716 Energy use with Advantix 1,055,080 909,401 765,377 666,599 681,218 594,714 585,162 Conventional energy bill 116,520$ 98,498$ 80,391$ 67,902$ 70,364$ 60,194$ 58,744$ Energy bill with Advantix 94,957$ 81,846$ 68,884$ 59,994$ 61,310$ 53,524$ 52,665$ Annual savings (%) 18.5% 16.9% 14.3% 11.6% 12.9% 11.1% 10.3%Annual savings ($) 21,563$ 16,652$ 11,507$ 7,908$ 9,055$ 6,670$ 6,080$

We use TMY3 (Typical Metrological Year data from the ASHRAE/NOAA database) data for Miami to estimate what the annual savings:

• Our most likely savings, the central finding of this pilot, is 18.5%• Our 90% confidence band is 11.6%‐23.3%

Additionally, assuming this model applies to these stores in other geographies we can estimate the annual savings elsewhere:

• Energy savings range from 10% to 18.5%• This would save $6,000‐$22,000 per store (assuming 9‐cents/kWh energy rates)

56

We can also use this data to calibrate our prospective energy calculator… which it turns out does quite well!

Without Advantix

With Advantix

Without Advantix

With Advantix

Miami 1,509,253 1,190,934 318,319 21% 1,294,671 1,055,080 239,591 19%Houston 1,044,528 790,907 253,621 24% 1,094,421 909,401 185,020 17%Wichita 751,229 579,497 171,732 23% 781,826 681,218 100,608 13%Baltimore 571,051 477,320 93,731 16% 754,468 666,599 87,869 12%Atlanta 751,140 659,579 91,562 12% 893,234 765,377 127,857 14%Boston 344,328 299,379 44,950 13% 652,716 585,162 67,554 10%Chicago 409,938 373,110 36,827 9% 668,820 594,714 74,106 11%

Home Depot Pilot ExtrapolationHVAC electricity use Energy

Savings (kWh/year)

Energy Savings (%)

HVAC electricity use Energy Savings

(kWh/year)

Energy Savings (%)

Representative city

Application engineering energy forecast tool

Our customary approach to calculating energy savings also uses TMY3, bins the weather data by temperature and humidity, and calculates loading per bin:

• Energy savings are similar ranging from 9%‐24%• We expect this approach to better model climates significantly different than Miami since we have not yet gathered moderate or cool weather data in this pilot

• This is evidenced by the larger energy use forecast with the pilot extrapolation in Boston and Chicago.

Despite these successes, overcoming barriers proves challenging

57

Behavioral 40% discount factor present, but technology clears hurdle rate often

Limited understanding of dehumidification, building dynamics, and how to apply technology

Barriers

Structural

Agency issuesTransaction barriersPricing distortionsOwnership transfer issues

Behavioral

Risk and uncertainty*Awareness& informationCustom and habitElevated hurdle rate

Availability

Adverse bundlingCapital constraintsProduct availabilityInstallationand use

Countless problems with contractors mis‐installing and failing to set up controls

Demand is difficult to forecast: oversold on some projects and lead times can grow

Even once a customer is convinced and job is specified sale is often delayed for funds

Not an issue given our short payback times and point of sale (i.e., new or replacement)

Complex market structure: owners, consulting engineers, manufacturing reps, & officials

Research, procurement and preparation time

Reps and manufacturers price strategically, the market isn’t about project‐level value pricing

Even once convinced few people want to be first in their area or application

Manifestation

Market has expectations that must be met: documentation, accessories, etc.

Educate the market place (“lunch & learns”, ASHRAE meetings, train staff and partners)

Develop a support team and offer an extended warrantee… often at no cost

Develop forecasting and manufacturing expertise

Hang in there. Pursue performance contracts where appropriate (reluctant ESCO)

Developed specific go‐to‐market strategy segmented by customer type (~2 years)Build up rep network and software tools to accelerate sales cycle (18+ months)Use similar tactics to win accounts that hold the keys to our success

Develop strategically located pilot installations with visitation rights (2‐4 years)

Develop the needed resources (at significant financial and human resource cost)

What we did about it

And did I mention… the need for excellent human resources

We’re hiring!

[email protected]

Date post:	19-Feb-2022
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Phil Farese Creating an Energy Efficient Future

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