Short-term wind power forecasting based on Elman neural network with particle swarm optimization

As a clean renewable energy, wind energy has become one of the new energy sources in China. However, because of the instability and volatility of the wind itself, the output of the wind power is also volatile. There is no guarantee of safety and stability of the power system in the acceptance of wind power, which can lead to a large number of abandoned power, so it is very important for the development of wind power to forecast the wind power. In this paper, because the neural network has the characteristic of self-learning, the Elman neural network is applied to predict the wind power, and to overcome the problem of local optimization, particle swarm optimization algorithm is used to optimize the Elman neural network.