mlsanityData to 14 Oct 2026

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

arXiv 2204.13702 ↗First posted 28 Apr 2022cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 28 Apr 2022.

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4 of 14 references.

References · 4

  1. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 402
  2. 2013Minimizing Finite Sums with the Stochastic Average Gradientcited by 164
  3. 2021Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networkscited by 8
  4. 2020LSTM-based Space Occupancy Prediction towards Efficient Building Energy Managementcited by 1

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