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.

翻译：以充电记录为输入的电动汽车充电需求模型，天然会偏向可用充电桩的供给。这些模型通常未能考虑因充电站被占用及竞争对手而损失的需求。损失的需求表明，实际需求可能高于充电记录所反映的情况，即真实需求是潜在（未观测到）的，而观测数据是删失的。因此，依赖这些观测记录来预测充电需求的机器学习模型，在未来的基础设施扩建和供给管理中的应用可能受限，因为它们并未估计充电的真实需求。我们提议使用考虑删失的模型来建模充电需求以解决这一局限。这些模型将删失纳入损失函数，并从观测的充电记录中学习真实的潜在需求分布。我们利用丹麦哥本哈根汽车的全球定位系统轨迹数据，研究了占用充电站和竞争服务如何导致需求删失。我们发现，在该市某些区域，删失发生的时间比例高达61%。我们利用研究中观测到的充电需求来估计真实需求，并发现考虑删失的模型相比未考虑删失的模型，能更准确地预测和估计实际需求的不确定性。我们建议，未来基于充电记录的充电模型应考虑删失，以扩展机器学习模型在供给管理和基础设施扩建中的应用领域。