Short-Term Wind Power Forecasting Based on Adaptive LSTM and BP Neural Network

To enhance power dispatching and mitigate grid connection fluctuations, this paper proposes a wind power prediction model based on Long Short-Term Memory-Back Propagation Neural Network (LSTM-BP) optimized by an adaptive Particle Swarm Optimization algorithm (aPSO). Initially, anomalies and missing values in raw wind farm data are addressed using the quartile method and filled via cubic spline interpolation. The data is then denoised using the Autoregressive Integrated Moving Average (ARIMA) model. Statistical and combined features are extracted, and Bayesian optimization is applied for optimal feature selection. To overcome the limitations of single models, a hybrid approach is adopted where a BP neural network is used in conjunction with LSTM. The optimal features are first input into the BP neural network to learn the current relationship between features and wind power. Then, historical data of both the features and wind power are fed into the LSTM to generate preliminary predictions. These LSTM outputs are subsequently passed into the trained BP neural network, and the final wind power prediction result is obtained through network integration. This combined model leverages the temporal learning capabilities of LSTM and the fitting strengths of BP, while aPSO ensures optimal parameter tuning, ultimately enhancing prediction accuracy and robustness in wind power forecasting. The experimental results show that the proposed model achieves a MAE of 0.54 MW and a MAPE of 10.5% in one-step prediction, reducing the error by over 35% compared to benchmark models such as ARIMA-LSTM and LSTM-BP. Multi-step prediction validation on 2000 sets of real wind farm data demonstrates the robustness and generalization capabilities of the proposed model.