Data-driven agent-based modeling, with application to rooftop solar adoption

Zhang, Haifeng; Vorobeychik, Yevgeniy; Letchford, Joshua; Lakkaraju, Kiran

doi:10.1007/s10458-016-9326-8

Title: Data-driven agent-based modeling, with application to rooftop solar adoption

Journal Article · Mon Jan 25 00:00:00 EST 2016 · Autonomous Agents and Multi-Agent Systems

DOI:https://doi.org/10.1007/s10458-016-9326-8· OSTI ID:1248850

Zhang, Haifeng ^[1]; Vorobeychik, Yevgeniy ^[1]; Letchford, Joshua ^[2]; Lakkaraju, Kiran ^[2]

Vanderbilt Univ., Nashville, TN (United States). Dept. of Electrical Engineering and Computer Science
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house- holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1248850

Report Number(s):: SAND-2015-2990C; PII: 9326

Journal Information:: Autonomous Agents and Multi-Agent Systems, Vol. 30, Issue 6; Conference: International Conference on Autonomous Agents and Multiagent Systems, 2015, Istanbul (Turkey), 4-8 May, 2015; ISSN 1387-2532

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 53 works

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