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Enhancing wind speed data imputation accuracy in wind resource assessment: Comparative study of emerging multivariate time series neural network models

Ming Li et al.

International Journal of Green Energy2026https://doi.org/10.1080/15435075.2026.2619437article
AJG 2
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0.50

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https://doi.org/https://doi.org/10.1080/15435075.2026.2619437

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@article{ming2026,
  title        = {{Enhancing wind speed data imputation accuracy in wind resource assessment: Comparative study of emerging multivariate time series neural network models}},
  author       = {Ming Li et al.},
  journal      = {International Journal of Green Energy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/15435075.2026.2619437},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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