LI Zimu,BAI Aijuan,WANG Hantao,et al.Research on Wind Speed Prediction in the Canyon Area of the Lower Jinsha River based on Long Short-term Memory Neural Network[J].Journal of Chengdu University of Information Technology,2025,40(05):626-632.[doi:10.16836/j.cnki.jcuit.2025.05.009]
基于长短期记忆神经网络的金沙江下游峡谷区风速预报研究
- Title:
- Research on Wind Speed Prediction in the Canyon Area of the Lower Jinsha River based on Long Short-term Memory Neural Network
- 文章编号:
- 2096-1618(2025)05-0626-07
- Keywords:
- LSTM algorithm; deep learning; canyon; wind speed prediction; gale
- 分类号:
- P457.5
- 文献标志码:
- A
- 摘要:
- 针对金沙江下游深切峡谷区大风频发且风速波动性强、预报难度大的问题,利用2022-2023年地面观测数据和ERA-5再分析数据,采用长短期记忆(LSTM)神经网络的深度学习算法,建立峡谷区未来1 h风速预报模型。模型建立中使用干季、湿季和全年3种数据集方案进行模型训练和预报效果检验。预报模型的检验表明,预报模型能够较好地反映峡谷区小时风速波动,对未来1 h风速的预报效果良好。对比3种数据集方案的风速预报结果,湿季和全年方案较优,预报和实际风速的相关系数分别为0.8和0.9,RMSE分别为1.5 m/s和1.6 m/s。模型对大风的预报效果明显降低,且预报风速低于实际风速,全年数据集方案相对较优,相关系数为0.6,RMSE为2.2 m/s。
- Abstract:
- In response to the challenges of frequent high winds and significant fluctuations in wind speeds in the deeply incised canyon area downstream of the Jinsha River,a wind speed forecasting model for the next hour was developed using ground observation data and ERA-5 reanalysis data from 2022 to 2023.The model employs a deep learning algorithm based on Long Short-term Memory(LSTM)neural networks.Three different datasets -dry season,wet season,and full year-were used for training the model and evaluating its forecasting performance.The evaluation of the forecasting model shows that it effectively captures the hourly wind speed fluctuations in the canyon area and provides good forecasts for wind speeds in the next hour.Comparing the results of the three dataset approaches,the wet season and full-year approaches were superior,with correlation coefficients of 0.8 and 0.9 respectively,and RMSEs of 1.5 m/s and 1.6 m/s respectively.The model’s performance in predicting high winds was notably poorer,with forecasted wind speeds being lower than actual wind speeds.The full-year dataset approach performed relatively better in this regard,with a correlation coefficient of 0.6 and an RMSE of 2.2 m/s.
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备注/Memo
收稿日期:2024-03-06
基金项目:国家自然科学基金资助项目(U2242202、U2040212); 中国气象局创新发展专项资助项目(CXFZ2022J012)
通信作者:白爱娟.E-mail:baiaj@cuit.edu.cn