Wind power prediction based on Elman neural network model optimized by improved genetic algorithm

摘要

Wind power prediction plays an important role in the impact analysis of wind farm connection to power grid. Aiming at the problems of the Elman neural network in wind power prediction, such as the difficulty in determining the structure and weight parameters and the low prediction accuracy, this paper proposes an improved genetic algorithm to optimize the Elman neural network model. The topological structure and weight parameters of the wind power prediction model based on Elman neural network are optimized and determined, and the effect of wind power prediction based on Elman neural network is also improved. The effectiveness of the proposed method is verified by comparing the simulation results with the real values.