Wind Power Prediction Using Artificial Neural Network Method

This MATLAB program implements wind power prediction using artificial neural network (ANN) methodology, enabling accurate forecasting of wind power output for multiple hours into the future. The system employs sophisticated machine learning algorithms that analyze and learn from historical wind power data patterns to predict future power generation trends with high precision. This capability is crucial for wind farm operations and dispatch management, allowing power companies to optimize generation scheduling and enhance grid stability and reliability. The implementation utilizes neural network architectures with backpropagation training algorithms, featuring input layers processing meteorological data and historical power records, hidden layers with activation functions for pattern recognition, and output layers generating power predictions. The program includes data preprocessing modules for normalization and feature selection, along with model validation components to ensure prediction accuracy. The system boasts a user-friendly interface with intuitive controls and visualization tools, enabling operators to easily configure prediction parameters, run simulations, and obtain reliable results through straightforward operations. The interface incorporates graphical displays of prediction curves, statistical analysis panels, and export functionality for reporting purposes. This ANN-based wind power prediction program represents a highly practical technical tool that significantly contributes to the development and operational efficiency of the wind energy industry, providing substantial assistance in power generation planning and grid management.