Short-term Load Forecasting Based on Aggregated Secondary Decomposition and Informer
A short-term load forecasting method based on aggregated Secondary modal decomposition and Informer is proposed to address the issue of non-stationarity in regional load and the low prediction accuracy of long sequences.Initially,the load sequence undergoes a preliminary decomposition using the improved complete ensemble EMD with adaptive noise(ICEEMDAN),tempering the original sequence's randomness and volatility.Subsequently,based on the entropy calculations of the sub-sequences,they aggregate,and by comparing various aggregation methods,the optimal reconstruction scheme is selected.The variational modal decomposition is employed to decompose the high-complexity co-modal functions further.Considering the impacts of electricity prices and meteorological factors on the load,the Random Forest(RF)algorithm is used for correlation analysis,constructing distinct high-coupling feature matrices for each sub-sequence and inputting them into the Informer for modeling.This enhances the forecasting efficiency of the load sequence through its multi-level encoding and sparse multi-head self-attention mechanisms.Ultimately,using the Barcelona regional-level load dataset for empirical verification,the findings affirm the prowess of the introduced framework in adeptly addressing the conundrums of modal overlap and high-frequency components encountered during modal decomposition.Furthermore,in a comparative analysis with revered deep learning paradigms,it manifests a commendable reduction of up to 65.28％in the root mean square error of long-sequence prediction.
万方数据
维普
