Wind power prediction based on denoising recurrent neural network model

Aiming at time series characteristics of wind power data, the Denoising Recurrent Neural Network model is proposed to predict the wind power in the short and medium term of the electric field. Through the model, the knowledge contained in it can be mined to improve the stability of the power system and optimize the power dispatching. An encoding-decoding structure is first designed in the recurrent neural network model, and the encoder is designed to obtain corresponding depth features from the sequence variables, and then the decoder decodes the depth features, restore the state of the input sequence and makes a prediction. Furthermore, the model designs the denoising module and prediction module in the decoder to overcome the difficulty of predicting noisy data with traditional recurrent neural networks, enabling the model to analyze input variables containing noise. By using the data collected by the power Internet of things to conduct experiments, the results show that the proposed method can forecast the wind power well and achieve a better prediction effect.