Autonomous Agents and Multi-Agent Systems

, Volume 30, Issue 6, pp 1023–1049 | Cite as

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

  • Haifeng ZhangEmail author
  • Yevgeniy Vorobeychik
  • Joshua Letchford
  • Kiran Lakkaraju


Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.


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



This work was partially supported by the U.S. Department of Energy (DOE) office of Energy Efficiency and Renewable Energy, under the Solar Energy Evolution and Diffusion Studies (SEEDS) program.


  1. 1.
    Arrow, K. J. (1962). The economic implications of learning by doing. Review of Economic Studies, 29(3), 155–173.CrossRefGoogle Scholar
  2. 2.
    Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.CrossRefzbMATHGoogle Scholar
  3. 3.
    Berger, T., & Schreinemachers, P. (2006). Creating agents and landscapes for multiagent systems from random samples. Ecology and Society, 11(2), 19.Google Scholar
  4. 4.
    Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.zbMATHGoogle Scholar
  5. 5.
    Bollinger, B., & Gillingham, K. (2012). Peer effects in the diffusion of solar photovoltaic panels. Marketing Science, 31(6), 900–912.CrossRefGoogle Scholar
  6. 6.
    Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Supp 3), 7280–7287.CrossRefGoogle Scholar
  7. 7.
    Borghesi, A., Milano, M., Gavanelli, M., & Woods, T. (2013). Simulation of incentive mechanisms for renewable energy policies. In European conference on modeling and simulation.Google Scholar
  8. 8.
    Brown, D. G., & Robinson, D. T. (2006). Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society, 11(1), 46.Google Scholar
  9. 9.
    Chen, W., Wang, Y., & Yang, S. (2009). Efficient influence maximization in social networks. In Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 199–208).Google Scholar
  10. 10.
    Coughlin, J., & Cory, K. (2009). Solar photovoltaic financing: Residential sector deployment. National Renewable Energy Laboratory. Technical report.Google Scholar
  11. 11.
    CPUC: California solar initiative program handbook (2013).Google Scholar
  12. 12.
    Dancik, G. M., Jones, D. E., & Dorman, K. S. (2011). Parameter estimation and sensitivity analysis in an agent-based model of leishmania major infection. Journal of Theoretical Biology, 262(3), 398–412.CrossRefGoogle Scholar
  13. 13.
    Denholm, P., Drury, E., & Margolis, R. (2009). The solar deployment system (SolarDS) model: Documentation and sample results. National Renewable Energy Laboratory. Technical report.Google Scholar
  14. 14.
    Draper, N., & Smith, H. (1981). Applied Regression Analysis (2nd ed.). New York: Wiley.zbMATHGoogle Scholar
  15. 15.
    Duong, Q., Wellman, M. P., Singh, S., & Vorobeychik, Y. (2010). History-dependent graphical multiagent models. In Proceedings of the 9th international conference on autonomous agents and multiagent aystems (Vol. 1, pp. 1215–1222). International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
  16. 16.
    Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning., Springer series in statistics Stanford: Springer.zbMATHGoogle Scholar
  17. 17.
    Geroski, P. A. (2000). Models of technology diffusion. Research Policy, 29(4), 603–625.CrossRefGoogle Scholar
  18. 18.
    Golovin, D., & Krause, A. (2011). Adaptive submodularity: Theory and applications in active learning and stochastic optimization. Journal of Artificial Intelligence Research, 42, 427–486.MathSciNetzbMATHGoogle Scholar
  19. 19.
    Happe, K., Kellermann, K., & Balmann, A. (2006). Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator agripolis, its adaptation and behavior. Ecology and Society, 11(1), 49.Google Scholar
  20. 20.
    Harmon, C. (2000). Experience curves of photovoltaic technology. International Institute for Applied Systems Analysis. Technical report.Google Scholar
  21. 21.
    Huigen, M. G., Overmars, K. P., & de Groot, W. T. (2006). Multiactor modeling of settling decisions and behavior in the san mariano watershed, the Philippines: A first application with the mameluke framework. Ecology and Society, 11(2), 33.Google Scholar
  22. 22.
    Janssen, M. A., & Ahn, T. K. (2006). Learning, signaling, and social preferences in public-good games. Ecology and Society, 11(2), 21.Google Scholar
  23. 23.
    Janssen, M. A., & Ostrom, E. (2006). Empirically based, agent-based models. Ecology and Society, 11(2), 37.Google Scholar
  24. 24.
    Judd, S., Kearns, M., & Vorobeychik, Y. (2010). Behavioral dynamics and influence in networked coloring and consensus. Proceedings of the National Academy of Sciences, 107(34), 14978–14982.CrossRefGoogle Scholar
  25. 25.
    Kearns, M., & Wortman, J. (2008). Learning from collective behavior. In Conference on learning theory.Google Scholar
  26. 26.
    Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 137–146).Google Scholar
  27. 27.
    Lobel, R., & Perakis, G. (2011). Consumer choice model for forecasting demand and designing incentives for solar technology. Working paper.Google Scholar
  28. 28.
    McDonald, A., & Schrattenholzer, L. (2001). Learning rates for energy technologies. Energy Policy, 29(4), 255–261.CrossRefGoogle Scholar
  29. 29.
    Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton: Princeton University Press.zbMATHGoogle Scholar
  30. 30.
    North, M., Collier, N., Ozik, J., Tatara, E., Altaweel, M., Macal, C., et al. (2013). Complex adaptive systems modeling., Complex adaptive systems modeling with repast simphony New York: Springer.Google Scholar
  31. 31.
    Palmer, J., Sorda, G., & Madlener, R. (2013). Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation. Working paper.Google Scholar
  32. 32.
    Rai, V., & Robinson, S. (2014). Agent-based modeling of energy technology adoption: Empirical integration of social, behavioral, economic, and environmental factors. Working paper.Google Scholar
  33. 33.
    Rai, V., & Sigrin, B. (2013). Diffusion of environmentally-friendly energy technologies: Buy versus lease differences in residential PV markets. Environmental Research Letters, 8(1), 014022.CrossRefGoogle Scholar
  34. 34.
    Rand, W., & Rust, R. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193.CrossRefGoogle Scholar
  35. 35.
    Rao, K., & Kishore, V. (2010). A review of technology diffusion models with special reference to renewable energy technologies. Renewable and Sustainable Energy Reviews, 14(3), 1070–1078.MathSciNetCrossRefGoogle Scholar
  36. 36.
    Robinson, S., & Rai, V. (2014). Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach. Working paper.Google Scholar
  37. 37.
    Robinson, S., Stringer, M., Rai, V., & Tondon, A. (2013). GIS-integrated agent-based model of residential solar PV diffusion. Working paper.Google Scholar
  38. 38.
    Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press.Google Scholar
  39. 39.
    Thiele, J. C., Kurth, W., & Grimm, V. (2014). Facilitating parameter estimation and sensitivity analysis of agent-based models: A cookbook using NetLogo and R. Journal of Artificial Societies and Social Simulation, 17(3), 11.CrossRefGoogle Scholar
  40. 40.
    Torrens, P., Li, X., & Griffin, W. A. (2011). Building agent-based walking models by machine-learning on diverse databases of space-time trajectory samples. Transactions in GIS, 15(s1), 67–94.CrossRefGoogle Scholar
  41. 41.
    van Benthem, A., Gillingham, K., & Sweeney, J. (2008). Learning-by-doing and the optimal solar policy in california. Energy Journal, 29(3), 131–151.Google Scholar
  42. 42.
    Wunder, M., Suri, S., & Watts, D. J. (2013). Empirical agent based models of cooperation in public goods games. In Proceedings of the fourteenth ACM conference on electronic commerce (pp. 891–908). ACM.Google Scholar
  43. 43.
    Zhai, P., & Williams, E. (2012). Analyzing consumer acceptance of photovoltaics (PV) using fuzzy logic model. Renewable Energy, 41, 350–357.CrossRefGoogle Scholar
  44. 44.
    Zhang, H., Vorobeychik, Y., Letchford, J., & Lakkaraju, K. (2015). Data-driven agent-based modeling, with application to rooftop solar adoption. In International conference on autonomous agents and multiagent systems, (pp. 513–521).Google Scholar
  45. 45.
    Zhao, J., Mazhari, E., Celik, N., & Son, Y. J. (2011). Hybrid agent-based simulation for policy evaluation of solar power generation systems. Simulation Modelling Practice and Theory, 19, 2189–2205.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Haifeng Zhang
    • 1
    Email author
  • Yevgeniy Vorobeychik
    • 1
  • Joshua Letchford
    • 2
  • Kiran Lakkaraju
    • 2
  1. 1.Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA
  2. 2.Sandia National LaboratoriesAlbuquerqueUSA

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