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| Ultra-short-term wind speed prediction based on VMD-LSTM |
| DOI：10.19783/j.cnki.pspc.190860 |
| Key Words:ultra-short-term wind speed prediction variational modal decomposition intrinsic mode function denoising LSTM |
| Author Name | Affiliation | | WANG Jun | The College of Hohai, Chongqing Jiaotong University, Chongqing 400074, China | | LI Xia | The College of Hohai, Chongqing Jiaotong University, Chongqing 400074, China | | ZHOU Xidong | The College of Shipping and Ship Engineering, Chongqing Jiaotong University, Chongqing 400074, China | | ZHANG Kai | The College of Hohai, Chongqing Jiaotong University, Chongqing 400074, China |
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| Abstract:Wind speed has characteristics of non-linearity, non-stationarity and randomness. In order to improve the accuracy of ultra-short-term wind speed prediction, a new method based on VMD and LSTM is proposed. First, the VMD is used to decompose the wind speed sequence into a series of IMF to reduce the complexity and non-stationarity of the original data. Secondly, LSTM models with 1-step ahead wind speed prediction are established for each IMF. Finally, the prediction results of each IMF are superimposed to obtain the final predicted wind speed. The results show that the prediction accuracy of the proposed model is better than other typical wind speed prediction models, and the model has good performance in the prediction of ultra-short wind speed. This work is supported by Social People’s Livelihood Special Fund of Chongqing Municipal Science and Technology Commission (No. cstc2016shmszx30002). |
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