A new BP-LSTM hybrid wind power forecasting model based on improved SFLA optimization

Abstract

To improve the accuracy of short-term wind power prediction, this paper proposes a short-term wind power hybrid forecasting model, ISFLA-BP-LSTM, based on an improved Shuffled Frog Leaping Algorithm (ISFLA) optimized Back Propagation network (BP) and Long Short-term Memory network (LSTM). This method introduces an adaptive differential evolution operator to optimize local search operations, enhancing the ISFLA optimization algorithm's ability to escape "local optimal solutions" and speeding up convergence. Finally, the improved ISFLA algorithm is used to optimize the weights and biases of BP and the hyperparameters of LSTM separately, establishing short-term wind power prediction submodels. The final short-term wind power prediction values are obtained by integrating the power outputs of ISFLA-BP and ISFLA-LSTM. This paper validates the performance of ISFLA and ISFLA-BP-LSTM on five commonly used test functions and wind energy data collected from the Xiyi Mountain Wind Farm in the Guangxi Zhuang Autonomous Region of China. The experimental results indicate that ISFLA in this paper outperforms the original SFLA in terms of performance, and the ISFLA-BP-LSTM hybrid model exhibits higher prediction accuracy compared to the initial model.