A Neural Network Approach to Wind Speed Prediction

Wind speed is a key factor for wind energy generation and air pollution dispersion. However, current accuracy of short-term wind speed prediction is poor and leads to economic loss in wind power system. Therefore, improving the accuracy is urgent. A stack long short-term memory (LSTM) network with fully connected network was adopted in this study to predict wind speed. Rather than just using the related meteorological variables (e.g. wind direction, temperature, relative humidity, dew point temperature, pressure, etc.) and wind speed sequences, we also added the convection terms of the momentum equations. The mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the prediction’s performance. The results showed our new proposed method could obtain better accuracy than previous studies.