Research on Wind Speed Prediction Method Based on Feature Fusion

Shengjie Zhou; Fei Luo

doi:10.62051/ijcsit.v2n1.46

Authors

Shengjie Zhou
Fei Luo

DOI:

https://doi.org/10.62051/ijcsit.v2n1.46

Keywords:

Wind speed prediction; Multisource data; Feature fusion; Deep learning

Abstract

Wind speed occupies a central position in the study of atmospheric circulation and climate change, playing a crucial role in the accuracy and reliability of meteorological forecasting models. The current meteorological observation network, especially in remote and mountainous areas, faces challenges due to insufficient data resulting from low station density. This limitation hampers a comprehensive understanding of wind field dynamics, subsequently affecting the performance of forecasting models. To enhance the performance of meteorological prediction models, this study adopts an innovative approach that combines boxplot difference indexing and feature-level fusion techniques to filter key predictive features and reduce information redundancy. Furthermore, by integrating spatial location information, the contribution of geographic data to wind field prediction is enhanced, improving the quality of the fused data and thereby increasing the accuracy of model predictions. Experimental comparisons demonstrate that the integrated wind field dataset, post feature fusion, achieves better results across various deep learning models.

Downloads

Download data is not yet available.

References

Kalnay E. Atmospheric modeling, data assimilation and predictability[M]. Cambridge university press, 2003. [M].https://doi.org/10.1198/tech.2005.s326

Gustafsson N, Janjić T, Schraff C, et al. Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres[J]. Quarterly Journal of the Royal Meteorological Society, 2018, 144(713): 1218-1256.https://doi.org/10.1002/qj.3179

Sasaki Y. An ObJective Analysis Based on the Variational Method[J]. Journal of the Meteorological Society of Japan. Ser. II, 1958, 36(3): 77-88.https://doi.org/10.2151/jmsj1923.36.3_77

Sasaki Y. Some basic formalisms in numerical variational analysis[J]. Monthly Weather Review, 1970, 98(12): 875-883.https://doi.org/10.1175/1520-0493(1970)098<0875:SBFINV>2.3.CO;2

Dimet F X L, Talagrand O. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects[J]. Tellus A, 1986, 38 A(2): 97-110.https://doi.org/10.1111/j.1600-0870.1986.tb00459.x

Lynch P. The origins of computer weather prediction and climate modeling[J]. Journal of Computational Physics, 2008, 227(7): 3431-3444.https://doi.org/10.1016/j.jcp.2007.02.034

Di, R., Wang, X., Meng, X., et al. Research on visibility prediction method based on multisource feature fusion. China New Technologies and Products, 2022(11): 13-16.10.13612/j.cnki.cntp.2022.11.020.

Yang, X., Xiao, Y., Chen, S. Study on Wind Speed and Power Generation Prediction in Wind Farms. Proceedings of the Chinese Society for Electrical Engineering, 2005, 25(11): 1-5..https://doi.org/10.3321/j.issn:0258-8013.2005.11.001

Maddix D C, Wang Y, Smola A. Deep Factors with Gaussian Processes for Forecasting[M]. arXiv, 2018.http://arxiv.org/abs/1812.00098

Shin H, Rüttgers M, Lee S. Effects of spatiotemporal correlations in wind data on neural network-based wind predictions[J]. Energy, 2023, 279: 128068.https://doi.org/10.1016/j.energy.2023.128068

Lipton Z C, Kale D C, Elkan C, et al. Learning to diagnose with LSTM recurrent neural networks[J]. arXiv preprint arXiv:1511.03677, 2015. http://arxiv.org/abs/1511.03677

Agrawal S, Barrington L, Bromberg C, et al. Machine Learning for Precipitation Nowcasting from Radar Images[M]. arXiv, 2019.http://arxiv.org/abs/1912.12132

Li X, Gao H, Zhang M, et al. Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind[J]. Remote Sensing, 2021, 13(21): 4325.https://doi.org/10.3390/rs13214325

Chen G, Tang B, Zeng X, et al. Short-term wind speed forecasting based on long short-term memory and improved BP neural network[J]. International Journal of Electrical Power & Energy Systems, 2022, 134: 107365.https://doi.org/10.1016/j.ijepes.2021.107365

Shen Z, Fan X, Zhang L, et al. Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network[J]. Ocean Engineering, 2022, 254: 111352.https://doi.org/10.1016/j.oceaneng.2022.111352