Wind Power Prediction Using BP Neural Networks with NWP Meteorological Data

This article presents a comprehensive comparison between two BP neural network methodologies for wind power prediction: one incorporating NWP (Numerical Weather Prediction) data and another excluding NWP meteorological forecasts. To demonstrate these approaches effectively, we provide relevant datasets and practical application scenarios. Specifically, our implementation utilizes historical wind speed, temperature data, and other meteorological parameters to forecast wind power generation. The neural network architecture employs backpropagation algorithms for weight optimization, with input layer normalization techniques applied to handle diverse data scales. From a code implementation perspective, the NWP-enhanced model integrates weather forecast data through additional input neurons, while the basic model relies solely on historical patterns. We conduct comparative analysis of prediction outcomes using metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The evaluation covers performance differences in various weather conditions and time horizons, discussing advantages and limitations of each approach regarding computational efficiency and prediction accuracy. Through these comparisons and technical analyses, we aim to provide deeper insights into neural network-based prediction methodologies and their practical applications in the wind energy industry. The code implementation includes data preprocessing modules, network training routines with epoch control, and result visualization components for performance assessment.