# Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations

arXiv: https://arxiv.org/abs/1604.05472 · HTML: https://mlsanity.com/paper/1604.05472/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2016.

## Built on by (most cited first, 4 of 4)

- [Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge](https://mlsanity.com/paper/2306.06142/) (2023)
- [Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.07772/) (2021)
- [SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City](https://mlsanity.com/paper/2110.09452/) (2021)
- [Optimizing Electric Vehicle Charging Station Placement Using Reinforcement Learning and Agent-Based Simulations](https://mlsanity.com/paper/2511.01218/) (2025)

## Abstract (arXiv)

Effective placement of charging stations plays a key role in Electric Vehicle (EV) adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a mixed-packing-and- covering optimization framework that models competing concerns of the service provider and EV users; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and set cover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data.