[论文解读] Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand

Frederik Boe Hüttel, Filipe Rodrigues+1 more|arXiv (Cornell University)|Jan 16, 2023

Electric Vehicles and Infrastructure被引用 4

一句话总结

本文提出了一种考虑审查机制的机器学习模型，通过考虑因充电站占用和竞争服务导致的未观测需求，来估算潜在的电动汽车（EV）充电需求。利用哥本哈根内燃机汽车（ICE）的GPS轨迹数据，研究发现高达61%的需求受到审查，而考虑审查机制的模型——尤其是审查分位数回归——在预测和不确定性估计方面显著优于传统模型。

ABSTRACT

Electric vehicle charging demand models, with charging records as input, will inherently be biased toward the supply of available chargers. These models often fail to account for demand lost from occupied charging stations and competitors. The lost demand suggests that the actual demand is likely higher than the charging records reflect, i.e., the true demand is latent (unobserved), and the observations are censored. As a result, machine learning models that rely on these observed records for forecasting charging demand may be limited in their application in future infrastructure expansion and supply management, as they do not estimate the true demand for charging. We propose using censorship-aware models to model charging demand to address this limitation. These models incorporate censorship in their loss functions and learn the true latent demand distribution from observed charging records. We study how occupied charging stations and competing services censor demand using GPS trajectories from cars in Copenhagen, Denmark. We find that censorship occurs up to $61\%$ of the time in some areas of the city. We use the observed charging demand from our study to estimate the true demand and find that censorship-aware models provide better prediction and uncertainty estimation of actual demand than censorship-unaware models. We suggest that future charging models based on charging records should account for censoring to expand the application areas of machine learning models in supply management and infrastructure expansion.

研究动机与目标

• 解决由于充电站容量有限和竞争导致的观测数据审查而引发的电动汽车充电需求模型偏差问题。
• 证明在高占用率或竞争激烈的环境中，观测到的充电记录会系统性低估真实的潜在需求。
• 开发并验证能够从审查数据中学习真实需求分布的审查感知模型。
• 在现实的反事实情景下，评估审查感知模型与标准模型的性能差异。
• 通过估算未观测到的全部需求，为更准确的基础设施规划和运营决策提供支持。

提出的方法

• 将哥本哈根内燃机汽车（ICE）的GPS轨迹作为电动汽车用户行为的代理，假设这些车辆为电动汽车。
• 通过在不同充电站可用性和竞争假设下，模拟电池耗竭和充电行为，来构建电动汽车充电需求。
• 应用审查感知模型，包括Tobit模型和审查分位数回归，这些模型将审查机制纳入损失函数，以估算潜在需求。
• 构建反事实情景，重新评估相同行程在不同充电站占用率和竞争性充电服务商市场份额下的表现。
• 利用观测到的充电记录校准模型，同时明确建模由于充电站容量和竞争对手存在导致的需求上限。
• 使用拟合度和不确定性估计等指标验证模型性能，对比审查感知与未考虑审查的模型。

Figure 1: The figure shows the two instances of censoring of electric vehicle (EV) charging demand Left: The demand is censored due to lost opportunities, where EV drivers can not charge due to occupied charging stations. Right: The demand is censored due to competing services, where EV drivers can

实验结果

研究问题

• RQ1 在哥本哈根等城市环境中，由于充电站占用和竞争服务，电动汽车充电需求在多大程度上受到审查？
• RQ2 与传统机器学习模型相比，审查感知模型在预测潜在充电需求和估计不确定性方面表现如何？
• RQ3 审查对基础设施规划和运营决策中需求预测准确性有何影响？
• RQ4 在不同竞争性充电服务商市场份额的情景下，审查感知模型的性能如何变化？
• RQ5 在缺乏电动汽车专属数据的情况下，非电动汽车的GPS轨迹能否作为估算潜在电动汽车充电需求的可靠代理？

主要发现

• 在哥本哈根的某些区域，高达61%的潜在电动汽车充电需求因充电站占用和竞争而被审查。
• 审查感知模型，尤其是审查分位数回归，相比Tobit模型，能提供更好的拟合度和更可靠的潜在需求不确定性估计。
• 在竞争激烈或高占用率的情景下，审查感知模型在预测真实需求方面显著优于未考虑审查的模型。
• 研究表明，忽略审查机制会导致对实际需求的系统性低估，从而限制标准模型在基础设施规划中的实用性。
• 利用非电动汽车的GPS轨迹作为电动汽车行为的代理，可在无需实际电动汽车数据的情况下，实现对需求审查的现实反事实模拟。
• 结果表明，未来的电动汽车充电需求模型应整合审查机制，以提升准确性，并支持战略性基础设施扩展。

Figure 2: Illustration of the experimental procedure. Firstly, the GPS trajectories of Internal Combustion Engine (ICE) vehicles are modelled as EVs using Algorithm 1 (for detailed information, refer to Appendix A ). Subsequently, the observed charging demand and the true demand are derived from the