Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer.

Autor: Xinxin W; School of Management, Wuhan University of Science and Technoloy, Wuhan, China., Xiaopan S; School of Management, Wuhan University of Science and Technoloy, Wuhan, China., Xueyi A; School of Management, Wuhan University of Science and Technoloy, Wuhan, China., Shijia L; School of Management, Wuhan University of Science and Technoloy, Wuhan, China.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Sep 08; Vol. 18 (9), pp. e0289161. Date of Electronic Publication: 2023 Sep 08 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0289161
Abstrakt: Wind energy, as a kind of environmentally friendly renewable energy, has attracted a lot of attention in recent decades. However, the security and stability of the power system is potentially affected by large-scale wind power grid due to the randomness and intermittence of wind speed. Therefore, accurate wind speed prediction is conductive to power system operation. A hybrid wind speed prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Multiscale Fuzzy Entropy (MFE), Long short-term memory (LSTM) and INFORMER is proposed in this paper. Firstly, the wind speed data are decomposed into multiple intrinsic mode functions (IMFs) by ICEEMDAN. Then, the MFE values of each mode are calculated, and the modes with similar MFE values are aggregated to obtain new subsequences. Finally, each subsequence is predicted by informer and LSTM, each sequence selects the one with better performance than the two predictors, and the prediction results of each subsequence are superimposed to obtain the final prediction results. The proposed hybrid model is also compared with other seven related models based on four evaluation metrics under different prediction periods to verify its validity and applicability. The experimental results indicate that the proposed hybrid model based on ICEEMDAN, MFE, LSTM and INFORMER exhibits higher accuracy and greater applicability.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Xinxin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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