Short-Term Power Load Forecasting Using Wavelet Neural Networks

Using wavelet neural networks for short-term power load forecasting is a widely adopted methodology. This approach can be effectively implemented through MATLAB code that integrates wavelet transform techniques with neural network architectures. The provided MATLAB implementation demonstrates how to preprocess load data using wavelet decomposition to extract temporal features, followed by neural network training for prediction modeling. This code can serve as a valuable reference for researchers and professionals working on power load forecasting applications. You can utilize this implementation as a foundation to understand the integration of wavelet analysis and neural networks for short-term forecasting tasks. The code includes key components such as data normalization, wavelet coefficient calculation, network topology configuration, and backpropagation training algorithms. We hope this resource proves beneficial for your power system analysis and forecasting projects!