根据GIS数据预测PDKEEPAAA充电基础设施的普及程度
Predicting popularity of EV charging infrastructure from GIS data
摘要：
充电基础设施的可用性对于大规模采用电动汽车（EV）至关重要。充电模式和基础设施的利用不仅会影响能源需求、当地电网负荷，还会影响经济回报、停车政策和电动汽车的进一步采用。我们开发了一种数据驱动的方法，该方法利用从描述充电基础设施附近的城市环境和城市活动的GIS数据中编译的预测器，以探索与一套全面的衡量充电基础设施性能的指标的相关性。最适合的是由充电基础设施吸引的独特的访问者群体（受欢迎程度）的大小。因此，充电基础设施按受欢迎程度排名。给定充电点是否属于顶层的问题被提出作为二元分类问题，并且评估用l-1惩罚、随机森林和梯度提升回归树正则化的逻辑斯谛回归的预测性能。所获得的结果表明，所收集的预测因子包含可用于预测充电基础设施的普及度的信息。预测的意义，以及它们是如何与流行的探讨。所提出的方法可以用于通知充电基础设施部署策略。
英文摘要：
The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand, loading local power grids but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that is exploiting predictors compiled from GIS data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators measuring the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l-1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
