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Wind and Structures Volume 36, Number 5, May 2023 (Special Issue) pages 333-344 DOI: https://doi.org/10.12989/was.2023.36.5.333 |
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A multi-step wind speed prediction method based on WRF simulation, an optimized data-generating model, and an error correction strategy |
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Lian Shen, Lihua Mi, Yan Han, Chunsheng Cai, Kai Li and Lidong Wang
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| Abstract | ||
| Improving the accuracy of wind speed predictions is crucial to the scheduling plan and operating stability of the power grid system. However, few studies utilize the generative adversarial network (GAN) to implement wind speed predictions considering the influence of other meteorological factors. Additionally, the accuracy of wind speed predictions needs to be further improved, especially for multi-step wind speed predictions. Subsequently, a novel hybrid wind speed prediction model is proposed, including four modules: (1) data collection of the weather research and forecasting (WRF) simulation, (2) data generation of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and GAN with the generator of bidirectional long short-term memory (BLSTM), (3) an error correction strategy of the CEEMDAN and GANBLSTM, and (4) hyperparameters optimization of the grid search (GS) and particle swarm optimization (PSO). Three datasets are utilized to validate the forecasting accuracy of the proposed model. The verification results demonstrate that the forecasting performance of the proposed model outperforms other baseline models. Taking the mean absolute percentage error (MAPE) of the ten-step prediction for the three datasets as an example, the MAPE values are respectively 0.51%, 0.46%, and 0.55% with correction, leading to 9.16%, 9.77%, 9.59% lower than those without correction. Above all, the proposed model possesses excellent wind speed prediction accuracy, especially in multi-step wind speed predictions, due to its lower values of MAPE with similar coefficients of determination (R2 ) values. | ||
| Key Words | ||
| bidirectional long short-term memory; error correction; generative adversarial network; wind speed prediction; WRF simulation | ||
| Address | ||
| Lian Shen:School of Civil Engineering, Changsha University, Changsha 411022, China Lihua Mi:Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology, Changsha,410076, China Yan Han:1)School of Civil Engineering, Changsha University, Changsha 411022, China 2)Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology, Changsha,410076, China Chunsheng Cai:Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing, 211189, China Kai Li:Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology, Changsha,410076, China Lidong Wang:Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology, Changsha,410076, China | ||