A combined AR-kNN model for short-term wind speed forecasting

Wind speed forecasting is critical to and challenging for wind energy industry. We present a combined AR-kNN regression model for short-term wind speed forecasting. Historical samples are selected to train the coefficients of a k-nearest-neighbor (kNN) regression model in order to capture the current variation pattern of wind speed. The training samples of the kNN model are combined with the recent samples of an autoregressive (AR) model tracking recent correlation of wind speed. To verify the performance of the proposed model, we apply it to the data of 10-min wind speed, and compare it with the persistence model, the single AR model and the single kNN regression model. The simulation results demonstrate that the combined AR-kNN model is effective and generates the most accurate forecasting of wind speed in several cases.