Wavelet Neural Network for Short-Term Power Load Forecasting

Wavelet Neural Networks (WNN) represent a hybrid model combining wavelet analysis with neural networks, demonstrating outstanding performance in short-term power load forecasting. This approach effectively handles the non-stationary and multi-scale characteristics of power load data, significantly improving prediction accuracy through advanced signal processing and machine learning techniques.

Wavelet Transform and Feature Extraction Power load data typically exhibits distinct periodicity and random fluctuations, which conventional neural networks may struggle to capture. Wavelet transform performs multi-scale decomposition, breaking down raw load data into sub-sequences across different frequency bands for efficient feature extraction. For instance, low-frequency components reflect trend variations while high-frequency components contain random noise and sudden fluctuations. Implementation typically involves selecting appropriate wavelet bases (e.g., Daubechies, Haar) and decomposition levels using functions like wavedec in MATLAB or PyWavelets in Python.

Neural Network Modeling and Prediction The wavelet-decomposed sub-sequences serve as inputs to neural networks, substantially reducing data complexity. Common network architectures include BP neural networks, RBF networks, or LSTM networks, with selection depending on data characteristics. Normalized data inputs ensure stable neural network training, preventing dimensional inconsistencies from affecting model performance. Code implementation often involves defining network layers, activation functions, and optimization algorithms using frameworks like TensorFlow or PyTorch.

Importance of Data Normalization Power load data often contains features with varying magnitudes. Normalization techniques (Min-Max or Z-Score) ensure all inputs reside in comparable ranges, enhancing model convergence speed and stability. Normalized data facilitates more effective neural network learning by preventing certain features from dominating the training process due to large numerical values. Implementation typically requires preprocessing libraries like sklearn.preprocessing in Python.

Prediction Process Optimization The WNN forecasting workflow generally includes: data preprocessing (denoising, normalization), wavelet decomposition (selecting appropriate wavelet bases and decomposition levels), neural network training (adjusting hidden layer nodes, learning rates), and result reconstruction (superimposing sub-sequence predictions). Parameter optimization through experimental tuning can further improve short-term load forecasting accuracy. Code implementation often involves iterative hyperparameter tuning and cross-validation techniques.

The method's advantage lies in its adaptive extraction of multi-scale features from power load data, combined with neural networks' nonlinear fitting capability, making it particularly suitable for volatile and periodic short-term forecasting scenarios. The integration of time-frequency analysis with deep learning represents a robust approach for handling complex power system dynamics.