Preprints
https://doi.org/10.5194/egusphere-2023-945
https://doi.org/10.5194/egusphere-2023-945
31 May 2023
 | 31 May 2023

A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, Variational Mode Decomposition, Principal Component Analysis, and Random Forest: VMD-PCA-RF (version 1.0.0)

Shaohui Zhou, Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li

Abstract. Accurate wind speed prediction is crucial for the safe utilization of wind resources. However, current single-value deterministic numerical weather prediction methods employed by wind farms do not adequately meet the actual needs of power grid dispatching. In this study, we propose a new hybrid forecasting method for correcting 10-meter wind speed predictions made by the Weather Research and Forecasting (WRF) model. Our approach incorporates Variational Mode Decomposition (VMD), Principal Component Analysis (PCA), and five artificial intelligence algorithms: Deep Belief Network (DBN), Multilayer Perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), light Gradient Boosting Machine (lightGBM), and the Bayesian Optimization Algorithm (BOA). We first construct WRF-predicted wind speeds using the Global Prediction System (GFS) model output based on prediction results. We then perform two sets of experiments with different input factors and apply BOA optimization to debug the four artificial intelligence models, ultimately building the final models. Furthermore, we compare the forementioned five optimal artificial intelligence models suitable for five provinces in southern China in the wintertime: VMD-PCA-RF in December 2021 and VMD-PCA-lightGBM in January 2022. We find that the VMD-PCA-RF evaluation indexes exhibit relative stability over nearly a year: correlation coefficient (R) is above 0.6, accuracy rate (FA) is above 85 %, mean absolute error (MAE) is below 0.6 m/s, root mean square error (RMSE) is below 0.8 m/s, relative mean absolute error (rMAE) is below 60 %, and relative root mean square error (rRMSE) is below 75 %. Thus, for its promising performance and excellent year-round robustness, we recommend adopting the proposed VMD-PCA-RF method for improved wind speed prediction in models.

Journal article(s) based on this preprint

02 Nov 2023
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023,https://doi.org/10.5194/gmd-16-6247-2023, 2023
Short summary

Shaohui Zhou et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-945', Anonymous Referee #1, 07 Jul 2023
    • AC1: 'Reply on RC1', shaohui zhou, 10 Jul 2023
  • RC2: 'Comment on egusphere-2023-945', Anonymous Referee #2, 17 Jul 2023
    • AC2: 'Reply on RC2', shaohui zhou, 25 Jul 2023
  • RC3: 'Comment on egusphere-2023-945', Anonymous Referee #3, 24 Jul 2023
    • AC3: 'Reply on RC3', shaohui zhou, 26 Jul 2023
  • RC4: 'Comment on egusphere-2023-945', Anonymous Referee #4, 25 Jul 2023
    • AC4: 'Reply on RC4', shaohui zhou, 27 Jul 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-945', Anonymous Referee #1, 07 Jul 2023
    • AC1: 'Reply on RC1', shaohui zhou, 10 Jul 2023
  • RC2: 'Comment on egusphere-2023-945', Anonymous Referee #2, 17 Jul 2023
    • AC2: 'Reply on RC2', shaohui zhou, 25 Jul 2023
  • RC3: 'Comment on egusphere-2023-945', Anonymous Referee #3, 24 Jul 2023
    • AC3: 'Reply on RC3', shaohui zhou, 26 Jul 2023
  • RC4: 'Comment on egusphere-2023-945', Anonymous Referee #4, 25 Jul 2023
    • AC4: 'Reply on RC4', shaohui zhou, 27 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by shaohui zhou on behalf of the Authors (28 Jul 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (29 Jul 2023) by Mohamed Salim
RR by Anonymous Referee #2 (10 Aug 2023)
RR by Anonymous Referee #4 (15 Aug 2023)
RR by Anonymous Referee #3 (20 Aug 2023)
ED: Reconsider after major revisions (21 Aug 2023) by Mohamed Salim
AR by shaohui zhou on behalf of the Authors (31 Aug 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Sep 2023) by Mohamed Salim
RR by Anonymous Referee #2 (03 Sep 2023)
RR by Anonymous Referee #3 (06 Sep 2023)
ED: Publish subject to minor revisions (review by editor) (07 Sep 2023) by Mohamed Salim
AR by shaohui zhou on behalf of the Authors (08 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 Sep 2023) by Mohamed Salim
AR by shaohui zhou on behalf of the Authors (13 Sep 2023)

Journal article(s) based on this preprint

02 Nov 2023
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023,https://doi.org/10.5194/gmd-16-6247-2023, 2023
Short summary

Shaohui Zhou et al.

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Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indexes for 10 months remain relatively stable: accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.