Wind Speed Forecasting using ARMA and Boosted Regression Tree Methods: A Case Study

Accurate wind speed forecasting is essential for power dispatch scheduling and energy commitment of wind farms. As a conventional approach to predict wind speed, the Auto Regressive Moving Average (ARMA) models are only accurate for very short-term/short-term time horizon wind speed forecasts within 0-6 hours. To overcome this issue, in this paper, a machine learning-based approach, known as Boosted Regression Tree (BRT) algorithm, is developed for wind speed forecasting, and is compared with ARMA models at different time horizons. It is found that, as the forecasting time horizons increase, the BRT model outperforms the ARMA model significantly. Historical wind speed data measured from the Meter Station at the Saskatoon International Airport, Saskatoon, Canada in 2022 are used for wind speed forecasting.