Data-driven agent-based modeling, with application to rooftop solar adoption
Data-driven agent-based modeling, with application to rooftop solar adoption
复制标题
基于数据驱动的代理建模，应用于屋顶太阳能的采用
DOI：
10.1007/s10458-016-9326-8
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发表时间：
2015
影响因子：
1.9
通讯作者：
Kiran Lakkaraju
中科院分区：
文献类型：
--
作者：
Haifeng Zhang;Yevgeniy Vorobeychik;Joshua Letchford;Kiran Lakkaraju
基于代理的建模通常用于研究代理之间的相互作用所产生的复杂系统特性。然而，基于代理的模型往往没有明确的预测开发，一般不验证。因此，我们提出了一种新的数据驱动的基于代理的建模框架，其中个人的行为模型是通过机器学习技术学习，部署在多代理系统和验证使用的集体采用决策的坚持序列。我们将该框架应用于预测圣地亚哥县的个人和集体住宅屋顶太阳能采用，并证明了由此产生的基于代理的模型成功预测了太阳能采用趋势，并提供了一个有意义的量化其预测的不确定性。同时，我们构建了第二个基于代理的模型，其参数校准的基础上，其拟合的总采用地面真理的均方误差。我们的研究结果表明，我们的数据驱动的基于代理的方法的基础上最大似然估计大大优于校准的基于代理的模型。看到国家的最先进的建模方法的优势，我们利用我们的基于代理的模型，以帮助寻找潜在的更好的激励结构，旨在刺激更多的太阳能采用。虽然太阳能补贴的影响是相当有限的，在我们的情况下，我们的研究仍然表明，一个简单的启发式搜索算法可以导致更有效的激励计划比目前的太阳能补贴在圣地亚哥县和以前探索的结构。最后，我们研究了一类专门的政策，给予免费系统的低收入家庭，这是显着更有效的比任何激励为基础的政策，我们已经分析了日期。
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.