Deterministic and probabilistic interval prediction for wind farm based on VMD and weighted LS-SVM

The intermittency and volatility of wind power (WP) have restricted the large-scale integration of wind turbines into power systems. Therefore, many researches on the deterministic and probabilistic forecasting of WP are being more important for power system planning and operation. In this work, a deterministic and probabilistic interval prediction for wind farm based on variational mode decomposition (VMD) and weighted least squares support vector machine (LS-SVM) were suggested. VMD is proposed to handle the variability of the novel WP series. In order to overcome the influence on outliers and non-Gaussian error distributions, a weighted LS-SVM is adopted to build deterministic prediction model for WP. Besides, lower upper bound estimation (LUBE) method for probabilistic interval forecasting is presented to quantify the potential risks of the WP series. The LUBE of the optimal prediction intervals is calculated. In the comparative experiments, prediction intervals coverage probability (PICP), prediction intervals normalized average width (PINAW) and normalized average deviation (NAD) are demonstrated to appraise the probabilistic prediction of WP. The simulation results show that the proposed method has much greater performance than other methods.

Automated summary

The paper proposes a deterministic and probabilistic interval prediction method for wind farms based on VMD and LS-SVM, with the LUBE method used to quantify potential risks. Experimental results show that the method outperforms other approaches.