Predictive analysis of traditional, deep learning and ensemble learning approach for short-term wind speed forecasting

Renewable energy is increasing rapidly to reduce fossil fuel consumption, due to environmental pollution and limited availability of fossil fuels. Several renewable energy sources, such as wind, solar, tidal are available in nature. However, harvesting wind energy is significantly more carbon free. The intermittent nature of speed is a big challenge to power dispatch in the grid network. The pattern of consumption of is a new challenge which requires an intelligent grid for reliable operation. The wind speed forecast may be a solution of this problem from system operator aspect. Various forecasting models are present in the literature which offer methods for accurate forecasting. Traditional and AI based i.e. Ensemble Deep Learning models are presented in this paper for the one year wind data set. The models are trained on 70% data and the rest of 30% data is used for testing. Ensemble learning model i.e. XGBoost results are compared to ARIMA, LSTM and Random Forest. The performance of the models has been examined using error indices such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE) on the training and testing datasets. Although all of the models produced good outcomes, the results indicate enhanced performance of XGBoost in comparison to the other techniques.