Constructing a Wavelet Neural Network Load Forecasting Model

To build a more accurate wavelet neural network load forecasting model, the following implementation strategies can be adopted to improve convergence speed and prediction accuracy: 1. Optimize wavelet neural network architecture by selecting appropriate nodes for input layer (e.g., historical load data features), hidden layer (determining optimal neuron count through cross-validation), and output layer (forecasted load values). In MATLAB implementation, this involves configuring network.topology and setting layer dimensions using functions like newff or custom wavelet activation functions. 2. Employ advanced training functions such as Levenberg-Marquardt (trainlm) for medium-sized networks or Bayesian Regularization (trainbr) for improved generalization. Code implementation includes setting net.trainFcn property and configuring training parameters (epochs, learning rate) through net.trainParam to accelerate convergence while preventing overfitting. 3. Integrate preprocessing techniques like feature selection (using correlation analysis or wrapper methods) and data normalization (z-score or min-max scaling) to enhance forecasting accuracy. Implementation involves preprocessing data arrays before network training using functions like mapminmax or zscore, followed by wavelet decomposition for feature extraction. By implementing these strategies with proper code configuration, we can construct a comprehensive and accurate wavelet neural network load forecasting model, significantly improving the reliability and practical utility of prediction results through systematic parameter optimization and algorithm enhancement.