MATLAB Program for Short-Term Load Forecasting Based on Wavelet Neural Network

This program implements a short-term load forecasting method based on wavelet neural networks. The approach combines wavelet analysis with neural network algorithms to achieve more accurate prediction of load variations. We have developed this program using MATLAB programming language, leveraging its powerful numerical computation and data processing capabilities. The implementation involves key MATLAB functions such as wavelet decomposition (wavedec) for multi-resolution analysis of load data and neural network toolbox functions (feedforwardnet, train) for building and training the forecasting model. The program processes historical load data along with relevant influencing factors to generate future load predictions. This forecasting system is particularly valuable for power system operation and planning, enabling better adjustment of power supply and demand balance, thereby enhancing grid stability and operational efficiency. The code structure includes data preprocessing, wavelet feature extraction, neural network training, and prediction visualization modules.