Agent-Based Modeling of Energy Technology Adoption: Empirical Integration of Social, Behavioral, Economic, and Environmental Factors

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. We develop an integrated agent-based model of residential solar adoption.Model is based upon a theoretically-driven behavioral model and data collection.Multiple data-streams are merged to enable empirical initialization of agent states.We use a technology-specific social network, leveraging observed geographic patterns.Model is validated using multiple (temporal, spatial, and demographic) criteria.

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