[1]王 琨,高敬更,张勇红,等.基于LSTM神经网络的复合变量电动汽车充电负荷预测方法技术研究[J].工业仪表与自动化装置,2019,(01):27-31.[doi:1000-0682（2019）01-0000-00]
　WANG Kun,GAO Jinggeng,ZHANG Yonghong,et al.Study on forecasting method of charging load of hybrid variable electric vehicle based on LSTM neural network[J].Industrial Instrumentation & Automation,2019,(01):27-31.[doi:1000-0682（2019）01-0000-00]
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基于LSTM神经网络的复合变量电动汽车充电负荷预测方法技术研究

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2019年01期
页码:
27-31
栏目:
出版日期:
2019-02-15

文章信息/Info

Title:
Study on forecasting method of charging load of hybrid variable electric vehicle based on LSTM neural network
作者:
王 琨1; 高敬更1; 张勇红2; 魏立兵1; 李 鹏1; 杨春光1; 董智颖1
1. 国网甘肃省电力公司电力科学研究院；
2. 国网甘肃省电力公司，兰州 730070
Author(s):
WANG Kun1;  GAO Jinggeng1;  ZHANG Yonghong2;  WEI Libing1;  LI Peng1;  YANG Chunguang1;  DONG Zhiying1
1. State Grid Gansu Electric Power Research Institute;?
2.State Grid Gansu Power Company, Lanzhou 730070, China
关键词:
电动汽车; 负荷预测; LSTM
Keywords:
electric vehicle;  load forecasting;  LSTM
分类号:
U469.72;TM910.6
DOI:
1000-0682（2019）01-0000-00
文献标志码:
A
摘要:
随着电动汽车并网容量的不断增加，面向电动汽车充电负荷准确地开展功率预测对于并网电力系统的经济调度和优化运行意义重大。基于计算机交叉学科的深度学习领域算法不断进步，为准确构建电动汽车充电负荷模型提供高效工具。该文研究一种基于LSTM(long short-term memory)神经网络的复合变量电动汽车充电负荷预测方法，将电动汽车充电负荷历史数据进行预处理，采用LSTM网络对降维后的时间序列进行动态建模，完成电动汽车充电负荷预测。采用实际数据进行验证，结果证明所提方法的有效性。
Abstract:
With the growth of electric vehicle charging load integrated with power system, accurate load forecasting is essential to economic dispatching and optimal operation of PV system. The progress of algorithm from deep learning provides an effective method for refined analysis of electric vehicle charging load. A multivariate method for electric vehicle charging load forecasting based on LSTM(Long Short-term memory) was presented in this paper. It modeled the model from the viewpoint of time based on LSTM. The real data was applied to verify the accuracy of the proposed method.

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备注/Memo

备注/Memo:
收稿日期：2018-05-31
基金项目：国网甘肃省电力公司科技项目资助（522722160030）
作者简介：王琨(1988)，男，江苏徐州人，硕士，电力工程师，研究方向为电力系统及其自动化。
更新日期/Last Update: 2019-01-15