基于Spearman相关性阈值寻优和VMD-LSTM的用户级综合能源系统超短期负荷预测OA北大核心CSTPCD

The integrated energy system(IES)faces great difficulties because of the strong complexity of the multivariate load series of IES at the user level,which is readily influenced by external factors.For that reason,this paper proposes a load forecasting way based on Spearman correlation threshold optimization,which integrates with variational mode decomposition(VMD)and long short-term memory network(LSTM).To start with,the Spearman rank(SR)correlation coefficient is used to quantitatively calculate the correlation between multiple loads and between loads and other climate factors,and the optimal correlation threshold is determined through cyclic optimization.Then,the VMD algorithm is used to decompose the load characteristic series screened based on the optimal threshold into simpler,more stable the regular intrinsic mode function(IMF)components are input into the LSTM model together with the optimal meteorological characteristics for load forecasting.The effectiveness of the proposed method is verified by the actual data of a user level IES,and the result was indicative of that the way can validly improve the accuracy of the multivariate load forecasting of IES.