A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets

被引：0
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作者：
Memarzadeh, Gholamreza [1 ]
Keynia, Farshid [2 ]
机构：
[1] Department of Power and Control Engineering, Graduate University of Advanced Technology, Kerman, Iran
[2] Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
来源：
Energy Conversion and Management | 2020年 / 213卷
关键词：
In recent years; clean energies; such as wind power have been developed rapidly. Especially; wind power generation becomes a significant source of energy in some power grids. On the other hand; based on the uncertain and non-convex behavior of wind speed; wind power generation forecasting and scheduling may be very difficult. In this paper; to improve the accuracy of forecasting the short-term wind speed; a hybrid wind speed forecasting model has been proposed based on four modules: crow search algorithm (CSA); wavelet transform (WT); Feature selection (FS) based on entropy and mutual information (MI); and deep learning time series prediction based on Long Short Term Memory neural networks (LSTM). The proposed wind speed forecasting strategy is applied to real-life data from Sotavento that is located in the south-west of Europe; in Galicia; Spain; and Kerman that is located in the Middle East; in the southeast of Iran. The presented numerical results demonstrate the efficiency of the proposed method; compared to some other existing wind speed forecasting methods. © 2020 Elsevier Ltd;
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