Short-Term Power Load Forecasting Based on Modal Decomposition and GRU-XGBoost
The accurate short-term power load forecasting can effectively improve power system operations.In order to solve the problem of strong volatility and randomness of power load data affected by various factors,a load prediction method based on modal decomposition and mixed model is proposed in this paper.Firstly,the principal component analysis(PCA)method is used to process the eigenvector of the load,the redundant information is removed,and then the historical load is decomposed into simplified sub-sequences by using the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN).Secondly,the sample entropy(SE)is introduced to calculate the entropy value of the sub-sequence,and the random,detailed,low-frequency and trend components of similar sub-sequences are reconstructed to obtain the random,detailed,low-frequency and trend components,and the gated recurrent unit(GRU)of different structures is selected to predict the different component types,and then the extreme gradient boosting model(XGBoost)is used to fit the residuals of each component,and the predicted value of each recombination sequence is the sum of the GRU prediction value and the XBGoost fitting value,and the final predicted value of each sequence is obtained.The results show that the root mean square error(RMSE),mean absolute percentage error(MAPE)and mean absolute error(MAE)of the proposed model are 370.676 MW,99.07%and 246.89 MW respectively,which is significantly reduced compared with the single model and the hybrid model.
load forecastingprincipal component analysisCEEMDANsample entropygate control loop unitextreme gradient enhancement model
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