To ensure the green and low-carbon operation of the printing park,a thorough understanding and prediction of electricity demand is crucial.However,electricity load forecasting faces obstacles like data complexity and noise interference,which complicate the capture of nonlinear signal characteristics.In response,a method for forecasting electricity load in printing parks based on the CEEMDAN-VMD-BiGRU model was introduced.Initially,the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)was used to break down the electricity load data,after which the Permutation Entropy(PE)was computed for each mode component.Subsequently,the Variational Mode Decomposition(VMD)was employed to perform a secondary decomposition specifically on the high-entropy frequency mode component,with the goal of addressing non-stationarity within the sequence.Finally,each component was used as input for the Bidirectional Gated Recurrent Unit(BiGRU)neural network model,and the model results were linearly combined to obtain the final prediction.The study focused on the electricity load data of a printing park and compared the proposed model with the predictions of the BiGRU model and the CEEMDAN-BiGRU model.The experimental results indicated that the second-order decomposition model incorporating VMD achieves an R2 of 97.13%,with RMSE of 25.354 and MAE of 62.776.Compared to other models,the proposed prediction model demonstrates superior performance.
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