New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory

Wind speed predictions are essential for wind power management and wind farm operation. However, due to the high volatility and nonstationarity of measured wind data, it is often difficult to achieve an accurate prediction. This study proposes a hybrid approach that consists of two stages, i.e., data preprocessing and wind speed predicting, to improve the accuracy of short-term wind speed prediction. A preprocessing algorithm for the transformation and standardization of hourly mean wind speed is utilized to remove the non-Gaussian distribution of wind data and diurnal nonstationarity. Several statistical models and artificial intelligence models are then adopted in the second stage of the prediction process, including a persistence model, autoregressive model, autoregressive moving average model and backpropagation neural network. The proposed approach is developed based on the weighted averaging of these models and error optimization theory. Finally, wind speed data for 12 months from two meteorological towers located in Yanan, China, are investigated to demonstrate the effectiveness and accuracy of the proposed approach for multistep wind speed predictions, and its performance is then compared with several existing prediction models. The results indicate that the prediction accuracy improves significantly after preprocessing with the proposed approach, outperforming all the existing aforementioned models.