论文标题
基于邻居的优化逻辑回归机器学习模型用于电动汽车占用检测
Neighbor-Based Optimized Logistic Regression Machine Learning Model For Electric Vehicle Occupancy Detection
论文作者
论文摘要
本文提出了一种优化的逻辑回归机器学习模型，该模型预测了相邻站点的占用率，可以预测电动汽车（EV）充电站的占用。该模型在一天中的时间进行了优化。该模型接受了加利福尼亚大学圣地亚哥分校校园周围57个电动汽车充电站的数据培训，在预测占用率方面的平均准确性为88.43％，最高准确性为92.23％，表现优于持久性模型基准。
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.
