3 month project focused on identifying specific households which are likely to install solar panel system.

1. Who’s Got Solar?
David Stier
Principal
David Stier + Associates

2. Leverage cities’ open data
and apply machine learning
To show who’s got solar
To predict who wants solar
THE GOAL

3. Level the playing field for installers and
consumers
Reduce the cost of selling solar
Improve ease of selecting solar
Encourage cities to provide open data
BENEFITS

4. Using publicly available information
how well can we model
the adoption of solar panel systems
among residential home owners ?
THE PROJECT

5. HYPOTHESIS
Machine learning applied to parcel level
data will generate significantly greater
profit than applying a ‘naïve’ approach

6. NREL SEEDS program has funded over a dozen
studies to understand who will adopt residential solar
To date, no study has used parcel-level information as
main data source
BACKGROUND

7. H E L L O
I’m Boston
Study focused on solar panel use in Boston:
• 48 square miles | 660k+ population
• Largest city in New England | 23rd largest city in US
• Significant # of green initiatives at local level and the state
has been active sponsor of solar

8. 2 0 1 5 R E S I D E N T I A L S O L A R
M a r k e t s i z e ( i n s t a l l a t i o n s )
107,000
United States
8,700MA
650Boston
400Owner Occupied
Single Family home
BACKGROUND

9. 25
142
177
295
438
16
94
72
97
216
0
50
100
150
200
250
300
350
400
450
500
2011 2012 2013 2014 2015
BACKGROUND
Boston solar panel
installations
2011 - 2015
Single Family Two-Three Family

10. DATASETS CHOSEN

11. DP04 Selected Housing Characteristics 570
fields
B25096 Mortgage Status By Value 37 fields
B25093 AGE OF HOUSEHOLDER BY SELECTED MONTHLY OWNER COSTS AS A %OF HOUSEHOLD INCOME IN PAST 12 MONTH 63
fields
B01001 Population by Age 103 fields
B5001 Citizenship Status 17 fields
B19049 MEDIAN HOUSEHOLD INCOME IN PAST 12 MONTHS (INFLATION-ADJUSTED) BY AGE OF HOUSEHOLDER 15 fields
B25040 House Heating Fuel 25 fields
DATASETS CHOSEN
US CENSUS TRACT LEVEL DATA ON HOUSING AND POPULATION
Total of 7 tables with 830 fields

12. B Y T H E N U M B E R S
6datasets /1.4Mrecords /4kinstallations / 6Year period
Final cleaned dataset:
25,303 x 1,628
BY THE NUMBERS

13. GEOGRAPHIC DATA
STRUCTURING AND SUMMARIZING LOCATION DATA
STREETS ARE THE KEY
- S T R E E T N A M E A N D T Y P E

14. GEOGRAPHIC DATA
STRUCTURING AND SUMMARIZING LOCATION DATA
STREETS ARE THE KEY
Example: Number of parcels per block
- S T R E E T N A M E A N D T Y P E
- H O U S E N U M B E R
A l t h o u g h ‘ o r d e r e d ’ s i g n i f i c a n t
v a r i a t i o n s e x i s t
S t i l l u s e f u l f o r 0 - 1 0 0 i n c r e m e n t s

15. GEOGRAPHIC DATA
STRUCTURING AND SUMMARIZING LOCATION DATA
STREETS ARE THE KEY
Example: Half mile grid based on Lat/Lon
- S T R E E T N A M E A N D T Y P E
- H O U S E N U M B E R
A l t h o u g h ‘ o r d e r e d ’ s i g n i f i c a n t
v a r i a t i o n s e x i s t
S t i l l u s e f u l f o r 0 - 1 0 0 i n c r e m e n t s
- G R I D B A S E D G R O U P I N G
C r e a t e ½ m i l e s q . g r i d s ( s h o w n )
a n d a l s o ¼ m i l e s u b g r i d s

16. GEOGRAPHIC DATA
STRUCTURING AND SUMMARIZING LOCATION DATA
STREETS ARE THE KEY
2010 US Census Tracts
- S T R E E T N A M E A N D T Y P E
- H O U S E N U M B E R
A l t h o u g h ‘ o r d e r e d ’ s i g n i f i c a n t
v a r i a t i o n s e x i s t
S t i l l u s e f u l f o r 0 - 1 0 0 i n c r e m e n t s
- G R I D B A S E D G R O U P I N G
C r e a t e ½ m i l e s q . g r i d s ( s h o w n )
a n d a l s o ¼ m i l e s u b g r i d s
- S O C I A L / P O L I T I C A L G R O U P I N G
C e n s u s t r a c t ( F e d e r a l )C e n s u s t r a c t ( F e d e r a l )
N e i g h b o r h o o d ( M u n i c i p a l )

17. - S T R E E T N A M E A N D T Y P E
GEOGRAPHIC DATA
STRUCTURING AND SUMMARIZING LOCATION DATA
STREETS ARE THE KEY
- H O U S E N U M B E R
A l t h o u g h ‘ o r d e r e d ’ s i g n i f i c a n t
v a r i a t i o n s e x i s t
S t i l l u s e f u l f o r 0 - 1 0 0 i n c r e m e n t s
- G R I D B A S E D G R O U P I N G
C r e a t e ½ m i l e s q . g r i d s ( s h o w n )
a n d a l s o ¼ m i l e s u b g r i d s
- S O C I A L / P O L I T I C A L G R O U P I N G
C e n s u s t r a c t ( F e d e r a l )
N e i g h b o r h o o d ( M u n i c i p a l )
5,748 ¼ mile combos
5,369 address blocks
3,453 streets
761 ½ mile grids
165 census tracts
16 neighborhoods
}

18. SORT ALL BY % OF ADDRESS BLOCK WHO HAVE SOLAR
THEN SORT BY # OF PROSPECTS
NAÏVE MODEL

19. RANDOM FOREST
STRATIFIED SHUFFLE SPLIT
Hold out 2016 installations (n = 219)
Split 50/50 train/test

20. Feature-level contribution to classification path
Top 100 Features – grouped by source, type & aggregation level
‘Raw’ Engineered
Data Source
Parcel
level
Address
Block
(eg 0-100 Main)
Parcel
level
1/4
Mile
Census
tract
Neighbor
hood
1/2
Mile Total
permit 0.137 0.013 0.048 0.002 0.010 0.210
assessor 0.052 0.045 0.030 0.022 0.016 0.031 0.196
311 0.011 0.017 0.056 0.016 0.008 0.004 0.111
Assessor
webscrape 0.010 0.003 0.013
Top 100 0.073 0.201 0.099 0.087 0.026 0.035 0.010 0.530
Features that I thought might have more impact….
- Heat type: 145th
for address block average
- Roof type: 411th

21. FIND PROFIT IN
YOUR CONFUSION MATRIX
B U Y E R S
N O S A L E
G L A D I
P I T C H E D
T H E M
A L L TA L K
N O S A L E
Inaccuracy
=
opportunity

22. Machine Learning Naïve Method
Prediction
Threshold Sales # calls
Cumul.
Cost # calls Cumul. Cost
15% 85 1,181 $10,039 4,285 $36,423
10% 123 1,811 $15,394 7,856 $66,776
6.75% 146 2,421 $20,579 11,843 $100,666
5% 163 2,880 $24,480 13,749 $116,867
SAVE $90K WITH
MACHINE LEARNING
Assume direct sales cold call is $8.50/contact
(40 houses/hour | 5% at home | $17/hour)
$92,387
savings

23. PROOF OF CONCEPT
whowantssolar.com
Show solar installers which homeowners are most likely to
adopt solar in there area
Provide the methodology to run this analysis in new markets
Beta map showing Boston results

24. PROOF OF CONCEPT
whosgotsolar.com
Most people considering solar want to talk to someone in
their neighborhood who has already installed solar.
whosgotsolar.com shows homeowners the names &
addresses of nearby neighbors who have installed solar
‘shovel ready’ cities:
San Francisco, Seattle, LA County,
Austin, Minnesota, Washington DC,
New York City

25. Thank you!