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文章摘要
多信息耦合下的充电站信息预测方法研究
Research on Information Prediction Method of Charging Station Based on Multiple Information Interconnections
Received:June 03, 2020  Revised:May 20, 2022
DOI：10.19753/j.issn1001-1390.2023.09.009
中文关键词: 电动汽车  充电站  相关性分析  深度信念网络
英文关键词: electric vehicle  charging station  correlation analysis  deep belief network
基金项目:国家自然科学基金项目（ 51677004）
Author NameAffiliationE-mail
ZHAO Yuming Shenzhen Power Supply Co,Ltd zhaoym97@sina.com 
Li YuJing* Beijing jiaotong University 18126119@bjtu.edu.cn 
Su Su Beijing jiaotong University ssu@bjtu.edu.cn 
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中文摘要:
      为解决电动汽车用户在有充电需求时“充电站难寻”及充电时间等待问题，文章提出了多信息互联耦合下的电动汽车充电站运营状态预测方法，首先利用高德地图及e充网基于Python爬虫技术收集预测区域内的交通路况信息及充电站信息，分析所在地气象状况及周边交通状况与充电桩忙闲状态之间的相关性。最后采用深度信念网络预测模型对充电站的运营状态进行预测，以某充电站实际数据进行算例分析，结果表明所提出的预测模型能够更准确地对充电站内充电桩的使用数目进行预测，并验证该预测结果可为用户在未来短期时间段内的可用充电站提供依据，均衡充电站之间的设备利用率。
英文摘要:
      
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