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

Short-term load forecasting based on wavelet neural networks is a hybrid modeling method that combines wavelet transform and neural networks, suitable for accurate prediction of short-term power load in electrical systems. This approach utilizes wavelet transform to perform multi-resolution decomposition of original load data, extracts features across different time scales, and then inputs these features into neural networks for training and prediction, thereby enhancing the model's nonlinear fitting capability.

### Core Methodology Data Preprocessing: First, historical load data undergoes cleaning to eliminate the impact of outliers and missing data. Wavelet decomposition splits the load sequence into high-frequency detail components and low-frequency approximation components, facilitating the capture of local characteristics in load variations.

Feature Extraction: Appropriate wavelet basis functions (such as db4, sym8) are employed to perform multi-scale decomposition of the original signal, generating subsequences in different frequency bands. These subsequences more clearly reflect the periodicity, trend, and randomness of the load.

Neural Network Modeling: The decomposed subsequences serve as input to the neural network, typically using BP neural networks or RBF neural networks for training. The hidden layer structure of the neural network requires optimization through cross-validation to prevent overfitting.

Prediction and Reconstruction: After the neural network predicts each subsequence individually, the wavelet reconstruction algorithm combines the prediction results to obtain the final load forecast value.

### Advantages and Applications Strong Robustness: Wavelet transform effectively reduces noise interference, making it suitable for handling non-stationary load data. Adaptive Learning: Neural networks automatically adjust weights to accommodate complex patterns in load changes. Engineering Applicability: MATLAB's Wavelet Toolbox and Neural Network Toolbox enable rapid algorithm implementation, applicable in areas such as power dispatch and energy management.

The key to this method lies in the selection of wavelet basis functions, determination of decomposition levels, and optimization of the neural network structure. Proper parameter settings can significantly improve forecasting accuracy.