Data-Driven Agent-Based Modeling, with Application to Rooftop Solar Adoption

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 households. ...

• Type: Journal
• Year: 2015
• Where: ATAL
• Authors: Haifeng Zhang, Yevgeniy Vorobeychik, Joshua Letchford, Kiran Lakkaraju
• Keywords: ATAL 2015, Intelligent Agents