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

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

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

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Abstract

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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Agent-based modeling Innovation diffusion Machine learning Policy optimization Rooftop solar

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Zhang, Haifeng, Vorobeychik, Yevgeniy, Letchford, Joshua, Lakkaraju, Kiran. 2016-01-25. Data-driven agent-based modeling, with application to rooftop solar adoption. https://doi.org/10.1007/s10458-016-9326-8

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