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

被引:0
|
作者
Shaw, Sayan [1 ]
Chia, Keaton [1 ]
Kleissl, Jan [1 ]
机构
[1] Univ Calif San Diego, La Jolla, CA 92093 USA
关键词
Electric Vehicle (EV); logistic regression; Long Short-Term Memory (LSTM); machine learning; Neighbor-based Optimized Logistic Regression (NOLR); occupancy detection; standby power consumption;
D O I
10.1109/AIIOT54504.2022.9817240
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Machine learning-based solutions for occupancy detection ubiquitously use multilayer Long Short-Term Memory (LSTM) models to achieve high accuracy with large time series data. This paper presents a streamlined and optimized single-layer logistic regression machine learning model that predicts the occupancy of an Electric Vehicle (EV) charging station given the occupancy of neighboring stations. In addition to this new neighbor-based approach, 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.
引用
收藏
页码:106 / 110
页数:5
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