| 王子龙,黄 莉.基于改进图时间卷积网络的农村地区电动汽车充电负荷预测及其对农网的影响[J].电力需求侧管理,2024,26(5):88-93 |
| 基于改进图时间卷积网络的农村地区电动汽车充电负荷预测及其对农网的影响 |
| Electric vehicle charging load prediction in rural areas and its impact on rural power grids based on modified graph temporal convolutional network |
| 投稿时间：2024-05-09 修订日期：2024-07-11 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 05. 014 |
| 中文关键词: 农村电网 电动汽车 充电负荷预测 图卷积网络 时间卷积网络 |
| 英文关键词: rural power grid electric vehicle charging load forecasting graph convolutional network temporal convolutional network |
| 基金项目:国家电网有限公司科技项目（5400- 202318574A-3-2-ZN） |
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| 中文摘要: |
| 在新能源汽车下乡政策的有力引导下,电动汽车在农村地区的销量快速增长,然而农村电网地域分布广、供电线路长,充电负荷相对分散且难以预测。为此,提出了基于改进图时间卷积网络的农村地区电动汽车充电负荷预测模型。首先,基于图卷积网络构建农村电网图结构矩阵,以表征用户充电特征的空间信息并降低输入数据的维度。其次,引入时间卷积网络感知充电数据的时序信息,挖掘影响负荷预测的时序特征。然后,提出基于注意力机制的改进图时间卷积网络算法进行充电需求预测,对不同特征进行权重分配,提升模型对时空信息的融合学习能力。最后,基于算例结果验证所提方法在农村地区电动汽车充电负荷预测上的有效性,并进一步分析了不同电动汽车渗透率下充电负荷对农村电网的影响。 |
| 英文摘要: |
| Under the strong guidance of the new energy vehicles to the countryside policy, sales of electric vehicles in rural areas have grown rapidly. However, rural power grid is widely distributed, power supply lines are long, and charging load is relatively dispersed and difficult to predict. To this end, a charging load prediction model for electric vehicles in rural areas based on modified graph temporal convolutional network（MGTCN）is proposed. Firstly, a rural power grid graph structure matrix is constructed based on graph convolutional neural network to characterize the spatial information of user charging characteristics and reduce the dimension of input data. Secondly, a temporal convolutional network is introduced to perceive the time series information of charging data and mine the time series features that affect load forecasting. Then, an MGTCN algorithm based on attention mechanism is proposed for charging demand forecasting. The attention mechanism assigns different weights to each feature, and the model can adaptively learn network parameters. Finally, the effectiveness of proposed method in predicting electric vehicle charging load in rural areas is verified based on the example results, and the impact of charging load on rural power grids under different electric vehicle penetration rates is further analyzed. |
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