Load prediction and optimization of main transformer based on EEMD-BP neural network

The main transformer of a substation has highly dynamic and nonlinear characteristics, and its load is affected by various nonlinear factors and fluctuation characteristics, making load forecasting complex and difficult to obtain ideal results. Therefore, an optimization study on load forecasting method for main transformers in substations based on EEMD-BP neural network is proposed. Firstly, decompose the transient and steady-state composite data acquisition logic, construct the historical load data sequence of the main transformer, and then select the horizontal processing method and mean filling method to preprocess the historical load data; Finally, the integrated empirical mode decomposition method is used to decompose the non-stationary daily load sequence into multiple sets of component sequences with frequencies ranging from low to high, and the remaining component sequences are randomly combined to optimize the nonlinear characteristics of the input load data in the traditional BP neural network model, effectively achieving the optimization prediction of the load of the main transformer in the substation.The experimental results show that the design method can accurately predict the seasonal substation main transformer load based on the environmental impact, and the average prediction error is low, the RMSE value is 0.089, the MAPE value is 2.19 MW, and the AE value is 4.43 MW, which can effectively realize the accurate prediction of the substation main transformer load under the influence of environmental factors.