Wind and Structures

  • 제36권5호
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  • Pages.333-344
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  • 2023
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A multi-step wind speed prediction method based on WRF simulation, an optimized data-generating model, and an error correction strategy

  • Lian Shen (School of Civil Engineering, Changsha University) ;
  • Lihua Mi (Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology) ;
  • Yan Han (School of Civil Engineering, Changsha University) ;
  • Chunsheng Cai (Department of Bridge Engineering, School of Transportation, Southeast University) ;
  • Kai Li (Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology) ;
  • Lidong Wang (Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering, Changsha University of Science & Technology)
  • 투고 : 2022.06.12
  • 심사 : 2023.09.10
  • 발행 : 2023.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

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 GAN-BLSTM, 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.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (Grant No. 52178452, 51808059), the Science and Technology Innovation Program of Hunan Province (Grant No. 2021RC4031), the Natural Science Foundation of Hunan Province (Grant No. 2021JJ40587), the Training Program for Excellent Young Innovators of Changsha of China (Grant No. kq1905005), the project of Open Fund of Hunan Province Research Center for Safety Control Technology and Equipment of Bridge Engineering (18 KC04, 14KC07), the Educational Commission of Hunan Province of China (22A0596).

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