Short-Term Load Forecasting Based on Chaotic Theory and Elman Neural Network

This program implements a short-term load forecasting method combining chaotic theory and Elman neural networks, achieving outstanding prediction performance. By utilizing this program, you can easily accomplish electricity short-term load forecasting, and it's equally suitable for other types of time series prediction applications. The methodology integrates chaotic theory with neural networks, fully leveraging the nonlinear characteristics of chaotic systems and the powerful expressive capability of neural networks, thereby enhancing prediction accuracy and stability. The implementation involves phase space reconstruction techniques to identify chaotic characteristics in load data, followed by Elman neural network training that incorporates context units to maintain internal states for better temporal pattern recognition. Using this program enables better understanding of load variation trends, optimization of power dispatch and energy planning, and improvement of energy utilization efficiency. The code structure includes modules for data preprocessing, chaos characteristic analysis (using Lyapunov exponents or correlation dimension calculations), and Elman network configuration with backpropagation through time training. Whether in the power industry or other domains, this program offers broad application prospects, providing increased convenience and benefits for your work.