Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection
This addresses the problem of efficient EV charging station management for users and operators, but it is incremental as it applies an optimized existing method to a specific dataset.
The paper tackled predicting Electric Vehicle charging station occupancy using neighboring station data and time of day, achieving an 88.43% average accuracy and 92.23% maximum accuracy, outperforming a persistence model.
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.