Short-Term Load Prediction Using Genetic Algorithm and Artificial Neural Network

This document provides a comprehensive explanation of how the program utilizes genetic algorithms and artificial neural networks for short-term load prediction. The implementation features genetic algorithm optimization for parameter tuning, where chromosome encoding represents neural network weights and thresholds, followed by selection, crossover, and mutation operations to evolve optimal solutions. The artificial neural network component employs backpropagation training with optimized initial parameters from the genetic algorithm, enhancing convergence speed and prediction accuracy. Detailed instructions for extracting the compressed archive to access full documentation are included. Additional background information covers the rationale for selecting these algorithms, their synergistic operation in the prediction pipeline, accuracy metrics evaluation methodology, and potential application scenarios in power system load forecasting. These supplementary details aim to facilitate better understanding and effective utilization of the program's capabilities.