Building a BP Neural Network Load Forecasting Model

Establishing a BP neural network load forecasting model involves selecting appropriate network architecture (input layer, hidden layer, output layer) and optimizing wavelet neural network training functions to enhance convergence speed and prediction accuracy. This process includes implementing data preprocessing techniques, designing optimal network structures, and fine-tuning hyperparameters for improved model performance.