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

• URL: http://arxiv.org/abs/2204.13702v1
• Date: Thu, 28 Apr 2022 01:05:32 GMT
• Title: Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection
• Authors: Sayan Shaw, Keaton Chia, Jan Kleissl
• Abstract summary: The model was 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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