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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

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GIS-Integrated Agent-Based Modeling of Residential Solar PV Diffusion

Scott A. Robinson, Matt Stringer, Varun Rai, &

Abhishek Tondon

Energy Systems transformation


Agent Based Modeling

-> Time


Follow decision rules (functions)

Have memory

Perceive their environment


Are autonomous

From: Deffuant, 2002.


Agent Attribute Example: Wealth

PV Adoption by Quartile

Average Income by Quartile


Environment Example: Tree Cover

> 60% Tree cover

< 15% Tree cover


Agent Initialization: Small World Network of n% Locals, 1-n% Non-locals. Assign initial Attitude

Behavioral Model

No further activity

Are there PV owners in my network?

From: Watts, 1998.


Attitudebecomes socially informed: SIA

Modify SIA. Is SIA>= threshold?

RA: select one network connection. Is connection credible?

Financially capable? Wealth + NPV + PP (Control)





Focus Test Site:

One zip code in Austin, TX

7692 households

146 PV Adopters (1.9%) as of Q2 2012City of Austin had approx. 1750 PV Adopters

Time Period:

Q1 2008 – Q2 2012


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


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


Current Work

-> Time

Agent Class: Installers



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 Agent-Based 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

COA Parcel Data

+ Home value

+ Address

+ Land Use

+ Sq. footage

GIS of Parcels

+ Coordinates


+ Geometry

+ Tree cover

Financial Model

+ Cash flows

+ Discount Rates

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: Key Batch Parameters

Energy Systems transformation