To further improve the accuracy of short-term power load prediction,it is necessary to explore the hidden nonlinear rela-tionships in load data at a deeper level.A short-term memory neural network based on quadratic modal decomposition of signal decom-position technology was proposed for short-term power load forecasting.Firstly,the proposed algorithm decomposed the original load da-ta using the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)algorithm.Secondly,the strong non-stationary components in the CEEMDAN decomposed components were decomposed by the variational mode decomposition(VMD)algo-rithm.at the same time,the number of VMD decomposition was optimized by using the central frequency method,and then the load subsequences obtained after two decompositions were fed into long short-term memory network(LSTM)for prediction,and the predicted results of the obtained components were overlaid.The results indicate that the method proposed in this article has significantly improved the accuracy of short-term power load forecasting results and model performance.
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