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- W4385217557 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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." @default.
- W4385217557 created "2023-07-25" @default.
- W4385217557 date "2023-07-24" @default.
- W4385217557 modified "2023-09-27" @default.
- W4385217557 title "Comment on egusphere-2023-945" @default.
- W4385217557 doi "https://doi.org/10.5194/egusphere-2023-945-rc3" @default.
- W4385217557 hasPublicationYear "2023" @default.
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