Short-term Wind Speed Prediction Based on CNN-GRU Model

This paper proposes a new combined prediction model for short-term wind speed prediction. The article uses Numerical Weather Prediction (NWP) and actual wind speed as input to the CNN-GRU model. The normalization method is used to solve the problem of the difference in magnitude between different data types. In order to extract the data characteristics between wind direction, temperature, air pressure, numerical weather forecast wind speed and actual wind speed, a continuous data matrix is constructed. The processed data set is divided into training set and test set. First, the characteristics of the data set are extracted using a Convolutional Neural Network (CNN). The fully connected layer then processes the extracted features and inputs them to the GRU network. Finally, the final predicted wind speed is obtained through the output layer. In order to avoid the gradient dispersion caused by the Sigmoid, this paper uses the Relu as the activation function of the network. The CNN-GRU model is compared with the CNN model and the continuous method under the same conditions. The results show that the proposed CNN-GRU model has the best effect in short-term wind speed prediction.