Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features
文献类型：期刊论文
| 作者 | Wang, Meng2,3; Wang, Juanle1,2,3; Yu, Mingming4; Yang, Fei1,2,3 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2024-10-31 |
| 卷号 | 14期号:1页码:26219 |
| 关键词 | Wind resources Spatiotemporal wind forecasting Local convolution kernel Recurrent neural network Inner Mongolia region |
| DOI | 10.1038/s41598-024-78303-8 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation. |
| WOS研究方向 | Science & Technology - Other Topics |
| WOS记录号 | WOS:001346350300023 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209532]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Meng,Wang, Juanle,Yu, Mingming,et al. Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features[J]. SCIENTIFIC REPORTS,2024,14(1):26219. |
| APA | Wang, Meng,Wang, Juanle,Yu, Mingming,&Yang, Fei.(2024).Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features.SCIENTIFIC REPORTS,14(1),26219. |
| MLA | Wang, Meng,et al."Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features".SCIENTIFIC REPORTS 14.1(2024):26219. |
入库方式： OAI收割
来源：地理科学与资源研究所
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