Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations

TL;DR

This paper presents a novel framework combining demand prediction and placement optimization for EV charging stations, using multi-view learning and iterative heuristics to balance provider and user concerns with real data.

Contribution

It introduces a multi-view learning approach for demand prediction and a mixed optimization framework for station placement, addressing both provider and user needs.

Findings

Demand prediction accuracy improved with multi-view learning.

The placement heuristic effectively balances provider and user concerns.

Real-world data validates the approach's effectiveness.

Abstract

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.