A hybrid method for short-term wind speed forecasting based on Bayesian optimization and error correction

Abstract

Accurate wind speed forecast plays an important role in the safe and stable operation of large-scale wind power integrated grid system. In this paper, a new hybrid model for short-term wind speed forecasting based on hyperparameter optimization and error correction is proposed, where the forecasting period is 5, 10, and 15 min, respectively, for three sites. The empirical wavelet transform is used to decompose the original wind speed series. Then, the Elman neural network and kernel extreme learning machine, which adopt Bayesian optimization algorithm for hyperparameter optimization, are used as predictors for wind speed prediction and error processing, respectively. In addition, a new error correction model using wind speed as model input is proposed. In order to verify the performance of the proposed model, three datasets collected from different real-world wind farms in Gansu and Xinjiang were considered as a case study to comprehensively evaluate the prediction performance of ten forecasting models. The results reveal that the proposed model has higher prediction accuracy and better prediction performance than the contrast models.