Wind speed prediction model based on multiscale temporal-preserving embedding broad learning system

Abstract

The inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal-preserving embedding broad learning system (MTPE-BLS) is proposed. MTPE-BLS used the localised behaviour of wind speed data, which is simpler to model and analyse than global patterns. Firstly, frequency clustering-based variational mode decomposition (FC-VMD) is proposed to deal with the non-stationary wind speed data into multiple intrinsic mode functions (IMFs). Then, temporal-preserving embedding (TPE) is proposed to extract the underlying temporal manifold structure from the decomposed IMFs. Finally, the extracted features are mapped into the broad learning system (BLS) to establish an accurate prediction model. Experimental results on two real-world wind speed datasets demonstrate the best performance of the proposed MTPE-BLS model compared to that of others. Compared to the original BLS, the MTPE-BLS achieves significant improvements, reducing the root mean square error (RMSE) and mean absolute error (MAE) by an average of 48.57% and 47.72%, respectively.