Wind Power Forecasting Using Historical Data for Future Time Periods

This study employs historical wind power data to forecast future power values over specific time periods. Using MATLAB programming for algorithm implementation and Excel for data preprocessing, we apply three distinct forecasting methodologies: neural network simulation prediction, gray prediction modeling, and time series analysis. Each method leverages unique patterns extracted from historical data to generate wind power forecasts. The neural network implementation involves designing multilayer perceptron (MLP) architectures with appropriate activation functions, while the gray prediction method uses GM(1,1) modeling for small sample forecasting. Time series analysis incorporates ARIMA modeling with parameter optimization. Following prediction generation, comprehensive error analysis is performed using metrics like MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error). The results are benchmarked against industry standards to validate the reliability and accuracy of each forecasting approach. These methodologies enable more precise wind power forecasting, providing valuable references for future energy planning and grid management. The MATLAB code includes specialized functions for data normalization, model training, and prediction visualization, ensuring reproducible results across different datasets.