Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing

It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing condition for them to be smoothed. Meanwhile, further theoretical analysis is provided to discuss why the STOK method is potentially more accurate than the ARIMA method for wind speed time series prediction. Results show that the proposed method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of mean absolute error (MAE) and root-mean-square error (RMSE).

Automated summary

A hybrid strategy based on spatio-temporal covariance model combining STOK technology with ARIMA regression method is proposed for wind speed forecasting. Results show that this method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of MAE and RMSE.