Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection

TL;DR

This paper introduces an optimized logistic regression model that predicts electric vehicle charging station occupancy based on neighboring stations' data, achieving high accuracy and outperforming benchmarks.

Contribution

The paper develops a neighbor-based logistic regression model optimized for time of day, improving EV station occupancy prediction accuracy.

Findings

88.43% average accuracy in predictions

92.23% maximum accuracy achieved

Outperforms persistence benchmark

Abstract

This paper presents an optimized logistic regression machine learning model that predicts the occupancy of an Electric Vehicle (EV) charging station given the occupancy of neighboring stations. The model was optimized for the time of day. Trained on data from 57 EV charging stations around the University of California San Diego campus, the model achieved an 88.43% average accuracy and 92.23% maximum accuracy in predicting occupancy, outperforming a persistence model benchmark.