基于SSA-VMD-BiLSTM模型的充电站负荷预测方法OA北大核心CSTPCD

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.