计及多因素优化分解电动汽车充电站短期充电负荷预测研究  

Short term Charging Load Prediction Study of Electric Vehicle Charging Station Considering Multi factor Optimization Decomposition

作　　者：李瀚婷 汤旻安[1] 杨桐[1,2] 苏毅 王昶又 LI Hanting;TANG Minan;YANG Tong;SU Yi;WANG Changyou(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;School of Electrical Engineering,Lanzhou Institute of Technology,Lanzhou 730050,China;Gansu Vocational College of Architecture,Lanzhou 730050,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070 [2]兰州工业学院电气工程学院,兰州730050 [3]甘肃建筑职业技术学院,兰州730050

出　　处：《兰州交通大学学报》2025年第1期137-146,共10页Journal of Lanzhou Jiaotong University

基　　金：国家自然科学基金(62363022、61663021、71763025、61861025);甘肃省教育厅:优秀研究生“创新之星”项目(2025CXZX-662);甘肃省自然科学基金(23JRRA886);甘肃省教育厅:产业支撑计划项目(2023CYZC-35)。

摘　　要：电动汽车(electric vehicle,EV)充电行为存在强随机性与高波动性,使其充电站短期充电负荷预测精度较低,作为移动电力存储和负载资源参与车到网(vehicle to grid,V2G)服务中,其调度中心需要在短时间内预测EV的充电负荷来改善其对电网负荷的影响。为了提高EV充电站短期充电负荷预测精度,提出一种冠豪猪优化器变分模态分解双向长短期记忆神经网络(crested porcupine optimizer variational mode decomposition bidirectional long short term memory,CPO VMD BiLSTM)组合模型进行EV充电站短期充电负荷预测的方法。首先,考虑影响EV充电负荷的多种因素和历史充电站充电负荷共同构成输入特征矩阵。然后利用CPO算法对VMD其核心参数进行优化搜索,实现参数自适应优化设置。之后采用CPO VMD对历史充电负荷数据进行分解,弱化负荷的非平稳性,捕捉其局部特征。最后在BiLSTM模型中输入分解后的特征矩阵来实现充电站短期充电负荷的预测目标。以美国ANN DATA公开数据集中位于加州理工大学校园内EV充电站的历史充电负荷数据作为实际算例,与独立模型、未优化组合模型、优化组合模型进行对比,均方根误差(root mean squared error,RMSE)和平均绝对误差(mean absolute error,MAE)平均降低了41.23%和59.04%。因此,验证了提出方法在充电站充电负荷短期预测中精度的提高和实用性。The strong randomness and high volatility of electric vehicle(EV)charging behavior make the short term charging load prediction accuracy of its charging station low,and as a mobile power storage and load resource participating in the vehicle to grid(V2G)service,its scheduling centre needs to predict the charging load of the EV in a short period of time in order to improve its impact on the power grid load.In order to improve the accuracy of short term charging load prediction for EV charging stations,a Crested Porcupine Optimizer Variational Mode Decomposition Bidirectional Long Short Term Memory,(CPO VMD BiLSTM)combination model for short term charging load prediction of EV charging stations was proposed.Firstly,multiple factors affecting EV charging loads and historical charging loads of charging stations are considered to form the input feature matrix.Then the CPO algorithm is used to optimize and search the core parameters of VMD to achieve the adaptive optimization settings of parameters.After that,CPO VMD is employed to decompose the historical charging load data,weaken the non stationarity of the load and capture its local characteristics.Finally,the decomposed feature matrix is input into the BiLSTM model to achieve the prediction goal of short term charging load of charging stations.Using the historical charging load data of EV charging stations in the open data set of American ANN DATA,which is located in the campus of California Institute of Technology as a practical example,compared with independent models,non optimized combination models,and optimized combination models,the Root Mean Squared Error(RMSE)and Mean Absolute Error(MAE)are reduced by 41.23%and 59.04%on average.Therefore,the accuracy improvement and practicality of the proposed method in short term prediction of charging load in charging stations are verified.

关 键 词：电动汽车 充电站 神经网络 组合模型 负荷预测 

分 类 号：TM61[电气工程—电力系统及自动化]