# Mind the Gap: Modelling Difference Between Censored and Uncensored Electric Vehicle Charging Demand

arXiv: https://arxiv.org/abs/2301.06418 · HTML: https://mlsanity.com/paper/2301.06418/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems](https://mlsanity.com/paper/1808.08798/) (2018)
- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)
- [Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks](https://mlsanity.com/paper/2106.04986/) (2021)
- [Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes](https://mlsanity.com/paper/2001.07402/) (2020)
- [Generalized Multi-Output Gaussian Process Censored Regression](https://mlsanity.com/paper/2009.04822/) (2020)
- [Censored Quantile Regression Forest](https://mlsanity.com/paper/2001.03458/) (2020)
- [Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management](https://mlsanity.com/paper/2108.00858/) (2021)
- [Modeling Censored Mobility Demand through Quantile Regression Neural Networks](https://mlsanity.com/paper/2104.01214/) (2021)
- [Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand](https://mlsanity.com/paper/2106.10940/) (2021)
- [Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services](https://mlsanity.com/paper/1902.09745/) (2019)
- [Unboxing the graph: Neural Relational Inference for Mobility Prediction](https://mlsanity.com/paper/2201.10307/) (2022)
- [Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis](https://mlsanity.com/paper/2205.13496/) (2022)

## Built on by (most cited first, 3 of 3)

- [Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence](https://mlsanity.com/paper/2309.10186/) (2023)
- [Bayesian Active Learning for Censored Regression](https://mlsanity.com/paper/2402.11973/) (2024)
- [Diffusion-aware Censored Gaussian Processes for Demand Modelling](https://mlsanity.com/paper/2501.12354/) (2025)

## Abstract (arXiv)

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.