| 引用本文: | 李智,侯兴哲,刘永相,等.基于深度学习的充电站容量规划方法[J].电力系统保护与控制,2017,45(21):67-73.[点击复制] |
| LI Zhi,HOU Xingzhe,LIU Yongxiang,et al.A capacity planning method of charging station based on depth learning[J].Power System Protection and Control,2017,45(21):67-73[点击复制] |
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| 摘要: |
| 随着电动汽车充电设施建设的日益增长，区域内社会用户所需要的电动汽车供电容量的预测问题成为影响充电站建设规划的关键问题。结合深度学习的数据特征研究理论，提供了一种基于充电站容量影响因子的机器学习容量规划预测方法。该方法以充电站周边交通、区域发展和电网安全等环境影响因子为基础，训练并建立服务环境与充电需求的神经网络映射模型。实验表明，该模型对待建充电站周边环境影响因子进行分析后可以给出待建充电站的理想充电容量，从而解决待建充电站的充电容量定容问题。 |
| 关键词: 充电站定容 电动汽车 大数据 |
| DOI：10.7667/PSPC170732 |
| 投稿时间：2017-05-16修订日期：2017-08-17 |
| 基金项目:国家科技支撑计划课题(2015BAG10B00) |
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| A capacity planning method of charging station based on depth learning |
| LI Zhi,HOU Xingzhe,LIU Yongxiang,SUN Hongliang,ZHU Zhu,LONG Yi,XU Tingting |
| (State Grid Chongqing Electric Power Research Institute, Chongqing 401123, China) |
| Abstract: |
| With the increasing growth of charging facilities for electric vehicles, the power supply capacity prediction for electric vehicles required by the social users in a district become a key issue for charging station’s construction planning. Combined with the data feature theory of depth learning, a machine learning based capacity planning forecasting method based on charging station capacity impact factors is presented. The method, considering environment impact factors such as traffic around charging station, regional development, power grid security and so on as the basis, trains and builds a neural network mapping model of service environment and charging requirement. Experiments show that the model can give the ideal charging capacity of the to-be-built charging station after analyzing the impact factors of the to-be-built power station surroundings, which could solve the sizing problem of the station charging capacity. |
| Key words: charging station capacity electric vehicle big data |
