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Missing data recovery of wind speed in wind farms: A spatial-temporal tensor decomposition approach
Missing data recovery plays a critical role in improving the data quality of wind speed in wind farms, and numerous methods have been proposed to address this issue. However, most of them suffer from the inability to fully use the information of known data, and thus, poor performance of recovery is usually achieved. In this paper, we propose a missing data recovery method based on spatial-temporal tensor decomposition. The proposed method rearranges the whole data based on discrete wavelet transform to construct a four-dimensional tensor of “site × week × scale × hour” for representing the spatial and temporal correlation of wind speed. A completeness tensor is estimated to impute missing data based on Tucker decomposition and the nonlinear conjugate gradient algorithm. The proposed method not only inherits the advantages of imputation methods based on the matrix pattern but also well mines the spatial and temporal inherent correlation of wind speed. Wind speed data of a wind farm are used to verify the effectiveness of the proposed method. The results show that the proposed method recovers missing data with much smaller mean absolute error and root mean square error and requires less effort for recovering missing data of fragmented or continuously, compared with the traditional methods.
Missing data recovery of wind speed in wind farms: A spatial-temporal tensor decomposition approach
Missing data recovery plays a critical role in improving the data quality of wind speed in wind farms, and numerous methods have been proposed to address this issue. However, most of them suffer from the inability to fully use the information of known data, and thus, poor performance of recovery is usually achieved. In this paper, we propose a missing data recovery method based on spatial-temporal tensor decomposition. The proposed method rearranges the whole data based on discrete wavelet transform to construct a four-dimensional tensor of “site × week × scale × hour” for representing the spatial and temporal correlation of wind speed. A completeness tensor is estimated to impute missing data based on Tucker decomposition and the nonlinear conjugate gradient algorithm. The proposed method not only inherits the advantages of imputation methods based on the matrix pattern but also well mines the spatial and temporal inherent correlation of wind speed. Wind speed data of a wind farm are used to verify the effectiveness of the proposed method. The results show that the proposed method recovers missing data with much smaller mean absolute error and root mean square error and requires less effort for recovering missing data of fragmented or continuously, compared with the traditional methods.
Missing data recovery of wind speed in wind farms: A spatial-temporal tensor decomposition approach
Tan, Hang (author) / Lin, Shengmao (author) / Xu, Xuefang (author) / Shi, Peiming (author) / Li, Ruixiong (author) / Wang, Shuying (author)
2023-05-01
12 pages
Article (Journal)
Electronic Resource
English
| American Institute of Physics | 2024
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