GIS-Integrated Agent-Based Modeling of Residential
Solar PV Diffusion
Scott A. Robinson, Matt Stringer, Varun Rai, &
Abhishek Tondon
Energy Systems
transformation
Motivation
Agent Based Modeling
-> Time
Agents:
Follow decision rules (functions)
Have memory
Perceive their environment
Are heterogeneous
Are autonomous
From: Deffuant, 2002
.
Agent Attribute Example: Wealth
PV Adoption by Quartile
Average Income by Quartile
Agent Attribute: Wealth
Environment Example: Tree Cover
> 60%
Tree cover
< 15%
Tree cover
Behavioral Model
Agent Initialization: Small World Network
of n% Locals, 1-n% Non-locals. Assign initial Attitude
.
From: Watts, 1998
Are there PV
owners in my
network?
Yes
No further
activity
No
Attitude
becomes
socially
informed: SIA
ADOPT
RA: select one
network
connection. Is
connection
credible?
Financially
capable?
Wealth +
NPV + PP
(Control)
Modify SIA.
Is SIA >=
threshold?
Implementation
Focus Test Site:
One zip code in Austin, TX
7692 households
146 PV Adopters (1.9%) as of Q2 2012
City of Austin had approx. 1750 PV Adopters
Time Period:
Q1 2008 – Q2 2012
Methods:
Multiple runs in each batch to allow for
inherent randomness in network
initialization and interaction effects
Runs in a batch have identical parameters
Validation: Batches test different
parameters against real test site data.
Temporal Validation
Empirical
Many strong
interactions, radial
neighborhoods, 90%
local connections.
Adopters are EOHs.
Weak interactions,
contiguous
neighborhoods
More non-local
connections
Weak interactions
Few weak
interactions, no EOHs
Spatial Validation
Current Work
Agent Class:
Installers
-> Time
Summary
ABMs are virtual laboratories
PV diffusion is a complex process with rich interaction effects:
Agent behavior: theory of planned behavior
Agent networks: small world networks
Agent interaction: relative agreement algorithm
Multidimensional validation (space and time) allows the
robustness of the ABM to be tested against “ground truth” events.
Early testing:
Strong, monthly interactions
90% geographic locals.
2000ft radial neighborhoods
Existing adopters with low uncertainty in attitude.
Low RMSE (3.6), and accurate clustering (1 false positive).
Q&A
Selected References:
Robinson, S.A., Stringer, M, Rai, V., Tondon, A., "GIS-Integrated AgentBased Modeling of Residential Solar PV Diffusion,“ USAEE North America
Conference Proceedings 2013, Anchorage, AK.
Rai, V. and Robinson, S. A. "Effective Information Channels for Reducing
Costs of Environmentally-Friendly Technologies: Evidence from Residential PV
Markets," Environmental Research Letters 8(1), 014044, 2013
Rai, V. and Sigrin, B. "Diffusion of Environmentally-friendly Energy
Technologies: Buy vs. Lease Differences in Residential PV Markets,"
Environmental Research Letters , 8(1), 014022, 2013.
Rai, V., and McAndrews, K. “Decision-making and behavior change in
residential adopters of solar PV,” World Renewable Energy Forum, 2012, Denver,
CO.
Appendix: TPB
Other options:
•
•
•
•
•
•
Theory of Reasoned Action
Rational Choice
Continuous opinions, discrete actions (CODA)
Consumat Framework
Stages of Change
…and many more
Appendix: Relative Agreement Algorithm
From Deffuant et al. 2012.
Energy Systems
transformation
Appendix: Data Streams
AE Program Data
+ App. Status
+ Address
+ Date
+ System Specs
Financial Model
+ Cash flows
+ Discount Rates
COA Parcel Data
+ Home value
+ Address
+ Land Use
+ Sq. footage
GIS of Parcels
+ Coordinates
+ DEM
+ Geometry
+ Tree cover
UT Solar Survey
+ Sources of Info.
+ Decision-making
Agent:
• Attitude
• Uncertainty
• Wealth
• Home sq. footage
• Age of home
• Network
• PP
• Discount rate
Environment:
• Tree Cover
• Shade
• Electricity Price
Appendix: Model Design
Appendix: Seasonal Effects
Appendix: Key Batch Parameters
Batch
mu
EOHs
Locals
Relative
Agreement
Percent
Locals
AUC
mu
2
0.5
No
Radial
1x
90%
0.693
10
0.5
Yes
Contiguous
4x
90%
0.687
18
0.7
Yes
Radial
4x
90%
0.680
19
0.7
Yes
Radial
3x
90%
0.686
20
0.5
Yes
Radial
3x
90%
0.679
22
0.5
Yes
Radial
3x
80%
0.682
Energy Systems
transformation