Wind Power Prediction: Comparative Analysis of BP Neural Network Approaches with and without NWP Data

This research conducts a systematic comparison between two distinct BP neural network implementations for wind power prediction. The primary methodology integrates NWP numerical weather forecast data, while the alternative approach operates without meteorological inputs. Our implementation leverages MATLAB's neural network toolbox, where we configure a multilayer perceptron with backpropagation training algorithm. Key architectural parameters include sigmoid activation functions in hidden layers and linear output neurons, optimized through gradient descent with momentum. The data preprocessing phase involves normalization of wind power historical data using z-score standardization, while NWP data undergoes feature engineering to extract relevant meteorological parameters. We employ cross-validation techniques to prevent overfitting and ensure model generalizability. The comparative analysis evaluates performance metrics including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) across multiple real-world case studies. Our methodology section details the complete workflow from data acquisition through SCADA systems to predictive modeling. The code implementation features data segmentation into training, validation, and test sets with temporal consistency preservation. Algorithm enhancements include adaptive learning rate adjustment and early stopping criteria to optimize convergence. The findings demonstrate significant accuracy improvements when incorporating NWP data, particularly under volatile weather conditions. This research provides actionable insights for renewable energy grid integration, highlighting the critical role of meteorological factors in forecasting precision. The implications extend to operational efficiency in wind farm management and stability in power system planning, contributing to more reliable renewable energy integration strategies.