Explore MATLAB source code curated for "短期负荷预测" with clean implementations, documentation, and examples.
Short-term load forecasting typically refers to 24-hour daily load prediction and 168-hour weekly load forecasting. This article primarily focuses on predicting average daily load, with implementation approaches including time series analysis and machine learning algorithms.
This program implements short-term load forecasting through genetic algorithm optimization and artificial neural network modeling. The package contains detailed documentation that can be accessed by extracting the compressed archive, including code implementation specifics and algorithm configuration parameters.
Implementation of short-term load forecasting by leveraging MATLAB's Wavelet Toolbox for signal decomposition and Neural Network Toolbox for pattern recognition and predictive modeling
MATLAB implementation of short-term load forecasting using wavelet neural network approach
An Elman neural network-based prediction program designed for short-term load forecasting in electrical power systems, featuring recurrent network architecture for time-series analysis
This approach combines chaos theory with generalized regression neural networks for short-term load forecasting, achieving satisfactory predictive performance through nonlinear system analysis and machine learning implementation.
When applying chaos theory and neural networks for short-term load forecasting, selecting appropriate neural network inputs is critical. This MATLAB program implements an embedding dimension selection algorithm for chaotic time series analysis, featuring multiple approaches for neural network input configuration to enhance prediction accuracy.
This program implements short-term load forecasting using chaotic theory and Elman neural networks, delivering excellent prediction accuracy. It provides a ready-to-use solution for power system short-term load forecasting and can be equally applied to other time series prediction tasks. The implementation features phase space reconstruction for chaotic analysis and Elman's recurrent neural network architecture with feedback connections for capturing temporal dependencies.
Short-Term Load Forecasting (A load prediction program utilizing wavelet transform and support vector machine algorithms)
A comprehensive RBF neural network implementation designed for short-term load forecasting in power systems, featuring data preprocessing, network training, and prediction capabilities.