Data-driven agent-based modeling, with application to rooftop solar adoption
- 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.
- 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
Web of Science
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