Short-term Wind Power Prediction Model Based on GSA Optimized GRU Neural Network

The short-term prediction of wind power has an important role in the safe and stable operation of the power system. In order to achieve high-precision short-term wind power prediction, a short-term wind power prediction model based on the Gravitational Search Algorithm (GSA) optimized Gated Recurrent Unit (GRU) neural network is proposed. First, a GRU neural network is established, which uses a gated recurrent unit. Compared with the traditional recurrent neural network using traditional recurrent units, it overcomes the problems of gradient explosion and gradient disappearance that may occur in the traditional recurrent neural network; Then, the improved gravitation algorithm with strong global optimization ability is used to optimize the GRU neural network parameters, which effectively improves the prediction accuracy of the model. Through actual simulation analysis and comparison with traditional GRU neural network prediction model, LSTM neural network prediction model and Elman neural network prediction model, the proposed short-term prediction model of wind power has good accuracy and stability.