Short-term power load forecasting based on generative adversarial networks and EMD-ISSA-LSTM
Aiming at the inherent instability and nonlinearity of power load,which makes it difficult to improve the accuracy of short-term power load prediction.In this paper,we propose a short-term power load prediction method based on the combination of EMD and LSTM.First,the original signal is decomposed into a series of eigenmode functions and a residual quantity using EMD.Subsequently,all the components are input into the LSTM.To further improve the accuracy of load forecasting and optimize the generalization ability of the model,an improved sparrow search algorithm is introduced to optimize LSTM hyperparameters for large component signals,and a table generative adversarial network is introduced to generate new data samples for raw load data,forming a short-term power load forecasting method based on table generative adversarial network and EMD-ISSA-LSTM.Finally,the load data of the ninth mathematical modeling competition for electricians and the load data of a prefecture and city in Hunan Province containing distributed power sources are used to validate the effect,and the results show that the mean absolute percentage error of the model under the two datasets is 2.37%and 2.26%,respectively.The validity of the method is verified.
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