南方电网技术2024，Vol.18Issue(5)：75-84,10.DOI:10.13648/j.cnki.issn1674-0629.2024.05.008
考虑分时电价和充电利用率特征的大型电动汽车充电站负荷短期预测方法
A Short-Term Load Forecasting Method for large scale Electric Vehicle Charging Stations Considering Characteristics of Charging Utilization Rate and Time-of-Use Electricity Price
摘要
Abstract
Considering the impact of time-of-use electricity pricing and charging utilization characteristics on the load of electric vehicle charging stations,a short-term load forecasting method is proposed for large scale electric vehicle charging stations that integrates long short-term memory and support vector regression(LSTM-SVR).Firstly,a feature matrix is constructed that includes factors influencing the charging load such as time-of-use electricity pricing,charging utilization rates,meteorological information,and historical data on charging load power.Secondly,the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method is applied to decompose sequences of the feature matrix containing time-of-use electricity pricing and charging utilization rates,which enhances data diversity.This process also employs a combined correlation coefficient method for data reduc-tion and feature selection.Subsequently,the northern goshawk optimization(NGO)algorithm is used to optimize the hyperparameters of both LSTM and SVR,solving for weight coefficients and constructing the integrated LSTM-SVR model.Finally,the model is validated using data from a large scale charging station in a specific city.Comparative experiments demonstrate that considering time-of-use electricity pricing and charging utilization features can effectively improve the accuracy of electric vehicle charging station load forecasting by over 8%.Moreover,the proposed LSTM-SVR forecasting method further enhances prediction accuracy.关键词
短期负荷预测/电动汽车充电站/充电利用率/分时电价/长短期记忆网络/支持向量回归/自适应噪声完备经验模态分解Key words
short-term load forecasting/electric vehicles charging stations/charge utilization rate/time-of-use electricity price/long short-term memory/support vector regression/complete ensemble empirical mode decomposition with adaptive noise分类
动力与电气工程引用本文复制引用
王长春,王果,赵倩宇,王守相..考虑分时电价和充电利用率特征的大型电动汽车充电站负荷短期预测方法[J].南方电网技术,2024,18(5):75-84,10.基金项目
国家重点研发计划资助项目(2022YFB2403900) Supported by the National Key Research and Development Program of China(2022YFB2403900). （2022YFB2403900）
