Wind Power Prediction Using Neural Networks and Time Series Analysis

This MATLAB program implements wind power prediction by combining neural networks with time series analysis techniques. Through the integration of neural network modeling and time series analysis, we achieve more accurate forecasts of wind power variation trends and future development patterns. The program helps researchers better understand the volatility of wind power generation, providing valuable references and decision-making support for the wind energy industry. By predicting wind power output, we can optimize the planning and management of wind power generation equipment, thereby improving utilization rates and operational efficiency. This MATLAB implementation serves as a practical tool for researchers and engineers to understand and apply neural networks and time series analysis in wind power forecasting contexts. The program likely employs key functions such as neural network training algorithms (e.g., feedforward networks with backpropagation) and time series processing methods (e.g., ARIMA or seasonal decomposition) to handle temporal dependencies in wind power data. The implementation may include data preprocessing steps, feature engineering for historical power data, and validation techniques to assess prediction accuracy against actual measurements.