Prediction Model of Wind Speed and Direction using Convolutional Neural Network - Long Short Term Memory

Abstract

This paper proposes the prediction model of wind speed and direction using convolutional neural network - long short-term memory (CNN-LSTM). The proposed prediction model combines CNN, LSTM, and fully connected neural networks (FCNN) which are useful for getting high prediction accuracy of wind speed and direction for wind power. Performances of the prediction models are evaluated by using root mean square error (RMSE) between actual measurement data and predicted data. To verify the effectiveness of the proposed prediction model in comparison with that using FCNN, CNN, or LSTM model. The usefulness of the proposed prediction model is evaluated from the improvement of prediction accuracy for each season. The proposed prediction model using CNN-LSTM can improve 27.95 – 42.16% for wind speed and 28.71 – 35.15% for wind direction depending on the season in comparison with using the FCNN that is a higher accuracy than CNN and LSTM models, and also it indicates the strongest prediction model.