Agent-based modeling of energy technology adoption: Empirical integration of social, behavioral, economic, and environmental factors
Authored by Scott A Robinson, Varun Rai
Date Published: 2015
DOI: 10.1016/j.envsoft.2015.04.014
Sponsors:
United States Department of Energy (DOE)
Solar Energy Evolution and Diffusion Studies (SEEDS
Austin Energy
Platforms:
Python
R
Agent Analyst
ESRI ArcGIS software
Model Documentation:
Other Narrative
Flow charts
Mathematical description
Model Code URLs:
Model code not found
Abstract
Agent-based modeling (ABM) techniques for studying human-technical
systems face two important challenges. First, agent behavioral rules are
often ad hoc, making it difficult to assess the implications of these
models within the larger theoretical context. Second, the lack of
relevant empirical data precludes many models from being appropriately
initialized and validated, limiting the value of such models for
exploring emergent properties or for policy evaluation. To address these
issues, in this paper we present a theoretically-based and
empirically-driven agent-based model of technology adoption, with an
application to residential solar photovoltaic (PV). Using
household-level resolution for demographic, attitudinal, social network, and environmental variables, the integrated ABM framework we develop is
applied to real-world data covering 2004-2013 for a residential solar PV
program at the city scale. Two applications of the model focusing on
rebate program design are also presented. (C) 2015 Elsevier Ltd. All
rights reserved.
Tags
Simulation
Dynamics
networks
Innovation Diffusion
Decision-Making
Policy
systems
Persuasion
Planned behavior
Pv