Short-term Power Load Forecasting Using Wavelet Neural Networks

Wavelet Neural Networks (WNN) demonstrate exceptional effectiveness in short-term power load forecasting. This advanced prediction methodology integrates wavelet analysis algorithms with neural network architectures to accurately process normalized input data. The implementation typically involves wavelet transform functions (such as db4 or haar wavelets) for signal decomposition and neural network layers (often using backpropagation or Levenberg-Marquardt optimization) for pattern recognition. By leveraging wavelet analysis's multi-resolution capabilities and neural networks' adaptive learning properties, WNNs effectively capture complex temporal patterns and trends in power consumption data. Through iterative training on historical datasets using gradient descent algorithms, the model establishes precise mapping relationships between input features and load demands. This forecasting approach enables the power industry to optimize resource allocation, enhance grid management efficiency, and improve overall power supply reliability through predictive analytics. Key implementation components include data normalization preprocessing, wavelet coefficient extraction, hidden layer activation functions, and output regression modules.