Power Load Forecasting Using Wavelet Neural Networks
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Resource Overview
Power load forecasting implementation using wavelet neural networks, featuring wnne as the main program with supporting functions for comprehensive prediction analysis
Detailed Documentation
Utilizing wavelet neural networks for power load forecasting represents an advanced computational approach. This methodology employs a primary program named wnne, supported by various auxiliary functions that handle data preprocessing, wavelet decomposition, neural network training, and prediction generation. The implementation typically involves wavelet transformation for feature extraction from load data, followed by neural network processing for pattern recognition and forecasting. This combined approach enables more accurate electricity load predictions, facilitating improved planning and management of power resources. The technical framework ensures stable and reliable power supply management, making wavelet neural network-based load forecasting a valuable field for research and practical application in smart grid systems.
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