Short-term wind speed prediction using hybrid machine learning techniques

Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R-2. The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.

Automated summary

Wind energy is a potential renewable energy source globally. Accurate prediction of wind speed is crucial for estimating wind power accurately. Hybrid machine learning models were used in this study for short-term wind speed prediction, with LDMR model outperforming others in prediction accuracy and ELM model being computationally faster.