Wavelet Neural Network for Short-Term Load Forecasting

In power systems, load forecasting serves as a critical task that significantly impacts system reliability and stability. Wavelet Neural Networks (WNN) represent an emerging approach that integrates wavelet transform with neural networks to achieve more accurate short-term load predictions. The implementation typically involves preprocessing historical load data using wavelet decomposition (e.g., via MATLAB's wavedec function) to capture multi-resolution temporal features, followed by neural network training (using frameworks like PyTorch or TensorFlow) where adaptive parameter optimization occurs through backpropagation algorithms. By learning patterns from historical load data, WNNs autonomously adjust prediction model parameters—such as connection weights and wavelet function scales—through gradient descent optimization, thereby improving both prediction accuracy and reliability. Key implementation steps include: 1) Data normalization using Z-score standardization, 2) Wavelet packet decomposition for feature extraction, 3) Neural network architecture design with wavelet activation functions, and 4) Hybrid training algorithms combining Levenberg-Marquardt optimization with wavelet coefficient tuning. Consequently, WNNs demonstrate broad application prospects in power systems, and this technical discussion aims to assist readers interested in load forecasting methodologies.