Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction

Hua Li, Zhen Wang, Binbin Shan and Lingling Li ()
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Hua Li: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Zhen Wang: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Binbin Shan: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Lingling Li: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China

Energies, 2022, vol. 15, issue 22, 1-21

Abstract: The instability of wind power poses a great threat to the security of the power system, and accurate wind power prediction is beneficial to the large-scale entry of wind power into the grid. To improve the accuracy of wind power prediction, a short-term multi-step wind power prediction model with error correction is proposed, which includes complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), sample entropy (SE), improved beetle antennae search (IBAS) and kernel extreme learning machine (KELM). First, CEEMDAN decomposes the original wind power sequences into a set of stationary sequence components. Then, a set of new sequence components is reconstructed according to the SE value of each sequence component to reduce the workload of subsequent prediction. The new sequence components are respectively sent to the IBAS-KELM model for prediction, and the wind power prediction value and error prediction value of each component are obtained, and the predicted values of each component are obtained by adding the two. Finally, the predicted values of each component are added to obtain the final predicted value. The prediction results of the actual wind farm data show that the model has outstanding advantages in high-precision wind power prediction, and the error evaluation indexes of the combined model constructed in this paper are at least 34.29% lower in MAE, 34.53% lower in RMSE, and 36.36% lower in MAPE compared with other models. prediction decreased by 30.43%, RMSE decreased by 29.67%, and MAPE decreased by 28.57%, and the error-corrected three-step prediction decreased by 55.60%, RMSE decreased by 50.00%, and MAPE decreased by 54.17% compared with the uncorrected three-step prediction, and the method significantly improved the prediction accuracy.

Keywords: multi-step wind power prediction; error correction; complete ensemble empirical modal decomposition adaptive noise; sample entropy; improved beetle antennae search algorithm; kernel extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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