广东电力2024，Vol.37Issue(6)：53-61,9.DOI:10.3969/j.issn.1007-290X.2024.06.006
基于SSA-VMD-BiLSTM模型的充电站负荷预测方法
Load Prediction Method of Charging Station Based on SSA-VMD-BiLSTM Model
摘要
Abstract
With the popularity of electric vehicles,the pressure on the safe and stable operation of the power grid is increasing.In order to develop and implement an efficient demand response strategy,short-term load prediction of charging stations is particularly important.However,in view of the unstable changes of the power load of charging stations and the many influencing factors,the existing methods lack noise separation and smooth decomposition of the load data and targeted analysis of the decomposed load data.In order to further improve the forecasting accuracy of charging station load,this paper presents a short-term load prediction method based on sparrow search algorithm(SSA)optimization variational mode decomposition(VMD)algorithm combined with bidirectional long short-term memory neural network(BiLSTM).Firstly,the SSA algorithm is used to optimize VMD parameters,and then the unsteady load data is decomposed into aperiodic principal component in noise concentration and multiple smooth periodic components through VMD.In view of the different dependence of the two component data before and after,multiple periodic components are directly based on historical data and combined with BiLSTM model method for load prediction.For the aperiodic principal component in the noise concentration,the paper considers the uncertainty of load change,analyzes the main external causes and makes the prediction based on the characteristic factor data and BiLSTM model method.Finally,the comprehensive prediction results are obtained by means of result reconstruction.The proposed method is compared with other models by example analysis,considering the error evaluation parameters,to verify it has higher accuracy and reliability.关键词
充电站负荷/短期负荷预测/变分模态分解/麻雀搜索算法/双向长短期记忆神经网络Key words
charging station load/short-term load forecasting/variational mode decomposition(VMD)/sparrow search algorithm/bidirectional long and short memory neural network分类
信息技术与安全科学引用本文复制引用
林彦旭,高辉..基于SSA-VMD-BiLSTM模型的充电站负荷预测方法[J].广东电力,2024,37(6):53-61,9.基金项目
国家自然科学基金项目(52077107) （52077107）
国家电网公司总部科技项目(5400-202416211A-1-1-ZN) （5400-202416211A-1-1-ZN）
