phil farese creating an energy efficient future
TRANSCRIPT
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…
Unlocking Efficiency in the U.S. Economy, Granade et. Al; McKinsey & Company
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
Unlocking Efficiency in the U.S. Economy, Granade et. Al; McKinsey & Company
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%
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
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
Market: 0.1 quadsSavings: 0%
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
Market: 0.75 quadsSavings: 75%
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
Market: 0.60 quadsSavings: 75%
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
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
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?
SSL Multi‐Year Program Plan, March 2011, http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_mypp2011_web.pdf
SSL Multi‐Year Program Plan, March 2011, http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/ssl_mypp2011_web.pdf
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
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
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
Source and copyright pending: Advantix Systems
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
Source and copyright pending: Advantix Systems
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
Source and copyright pending: Advantix Systems
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
Source and copyright pending: Advantix Systems
Liquid desiccant can cool and dry air simultaneously, without reheating, and regenerates with less energy
Source and copyright pending: Advantix Systems
40
Advantix equipment integrates a heat pump thereby using waste heat to regenerate its desiccant
Source and copyright pending: Advantix Systems
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
Source and copyright pending: Advantix Systems
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
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8/6/20
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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
Date of Lennox only cycle
Date of Advantix & Lennox cycle
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%!!
Date of Lennox only cycle
Date of Advantix & Lennox cycle
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%
Date of Lennox only cycle
Date of Advantix & Lennox cycle
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