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A Method Based on Lorenz Disturbance and Variational Mode Decomposition for Wind Speed PredictionZHANG, Y. , GAO, S. , BAN, M. , SUN, Y.
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wind speed prediction, atmospheric dynamics system, Lorenz system, artificial neural network
wind(27), speed(22), energy(18), forecasting(16), model(13), prediction(11), neural(11), novel(8), systems(7), renewable(7)
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About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02001
Web of Science Accession Number: 000475806300001
SCOPUS ID: 85066296866
Wind power is one of the most promising means of power generation. But the time-varying of wind speed is the most fundamental problem for power generation control system. Therefore, accurate wind speed prediction becomes particularly important. However, traditional wind speed predictions often lack consideration of the influence of atmospheric dynamic system. And few papers have introduced VMD method into the field of wind speed prediction. Thus, combined with four neural networks, this paper develops a wind speed prediction method based on Lorenz system and VMD, obtains LD-VMD-Elman wind speed prediction model. Simulation results show that: 1) As for wind speed prediction, Elman neural network has higher prediction accuracy and smaller error. 2) The models which added Lorenz disturbance can describe the actual physical movement of wind more accurately. 3) VMD can abstract the changing rules of different wind speed frequencies to improve the prediction effect. This paper makes up for the lack of consideration of atmospheric dynamic system. The Lorenz equation is used to describe the atmospheric dynamic system, which provides a new thought for wind speed prediction. The LD-VMD-Elman model significantly improves the accuracy of wind speed prediction and contribute to the power dispatch planning.
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Digital Object Identifier: 10.1007/s11356-020-08181-w [CrossRef]
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Digital Object Identifier: 10.1016/j.enconman.2019.112254 [CrossRef]
 Short-term wind speed prediction model based on GA-ANN improved by VMD, Zhang, Yagang, Pan, Guifang, Chen, Bing, Han, Jingyi, Zhao, Yuan, Zhang, Chenhong, Renewable Energy, ISSN 0960-1481, Issue , 2020.
Digital Object Identifier: 10.1016/j.renene.2019.12.047 [CrossRef]
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 Wind Speed Prediction Research Considering Wind Speed Ramp and Residual Distribution, Zhang, Yagang, Gao, Shuang, Han, Jingyi, Ban, Minghui, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2940897 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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