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应用曲线分群预测的电动汽车充电设施规划方法

张禄, 孙舟, 王伟贤, 李香龙, 周杨, 黄其进, 陈雁

张禄, 孙舟, 王伟贤, 等. 应用曲线分群预测的电动汽车充电设施规划方法[J]. 现代电力, 2018, 35(4): 21-26.
引用本文: 张禄, 孙舟, 王伟贤, 等. 应用曲线分群预测的电动汽车充电设施规划方法[J]. 现代电力, 2018, 35(4): 21-26.
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ZHANG Lu, SUN Zhou, WANG Weixian, et al. Electric Vehicle Charging Facility Planning Method Based on Curves Clustering Forecast[J]. Modern Electric Power, 2018, 35(4): 21-26.
Citation: ZHANG Lu, SUN Zhou, WANG Weixian, et al. Electric Vehicle Charging Facility Planning Method Based on Curves Clustering Forecast[J]. Modern Electric Power, 2018, 35(4): 21-26.
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应用曲线分群预测的电动汽车充电设施规划方法

  • 1.

    国网北京市电力公司电力科学研究院,北京 100075;

  • 2.

    北京中电普华信息技术有限公司,北京 100085

详细信息
    作者简介:

    张禄(1984—),男,博士,研究方向为电动汽车充换电及运营管理数据分析技术;
    E-mail:zhanglu7@163.com;
    孙舟(1986—),男,硕士,研究方向为电动汽车充换电及运营管理数据分析技术;
    E-mail:sunzhou0812@163.com。

  • 中图分类号: TM91

Electric Vehicle Charging Facility Planning Method Based on Curves Clustering Forecast

  • 1.

    State Grid Beijing Electric Research Institute, Beijing 100075, China;

  • 2.

    Beijing China-power Information Technology CO., LTD, Beijing 100085, China

摘要

    摘要
  • 摘要: 针对电动汽车公共充电设施的建设规划问题,以充电设施的建设数量满足电动汽车的充电需求为目标,建立电动汽车充电需求预测模型。采用基于充电电量曲线聚类的分群预测方法,应用霍普金斯统计量评估曲线聚类趋势,结合肘方法(elbow method)来选择合理的聚类数;考察各类典型充电电量曲线趋势和扰动方面的特征并选取适用的预测算法(Holt-Winters指数平滑、ARIMA模型),有效提高了电动汽车充电需求预测的准确性。采用某市15个行政区县的电动汽车充电数据进行实例分析,分析结果表明,利用此模型对电动汽车公用充电设施的充电需求进行预测,准确量化公共充电设施的建设规模,提高了公共充电设施投放的针对性。
    Abstract: In view of the planning and construction of electric vehicle (EV) public charging facilities, a charging demand forecast model is established with the objective of the size of charging facilities meeting the charging demand of EV. The cluster prediction method is applied based on clustering of charging curves, the clustering trend of charging curves is evaluated by Hopkins statistic, and cluster numbers are selected reasonably by elbow method. Then, the trend and irregular characteristics of every clusters are investigated and appropriate predict method, such as Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA), is chosen. In the end, charging curves of EV from 15 counties and districts of one city are taken as simulation case, the analysis results show that the proposed model increased the predict accuracy of charging demand effectively, and increased the precision of siting and sizing of public charging facilities.
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    • 参考文献(14)
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出版历程
  • 收稿日期:  2017-11-16
  • 修回日期:  2018-07-29
  • 发布日期:  2018-07-08

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