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

•Improve the accuracy and speed of the short term wind speed forecasting by a hybrid forecasting model.•Propose a feature selection method based on entropy and mutual information technique.•Implement a deep learning time series prediction model based on LSTM.•Using the Wavelet Transform module elimi...

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Vydáno v:Energy conversion and management Ročník 213; s. 112824
Hlavní autoři: Memarzadeh, Gholamreza, Keynia, Farshid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.06.2020
Elsevier Science Ltd
Témata:
accuracy
administrative management
algorithms
artificial intelligence
behavior
Clean energy
Crow search algorithm
Deep learning
Electric power generation
Electric power grids
energy conversion
Energy sources
Entropy
Feature selection
Forecasting
information
Iran
Long short term memory
Machine learning
Middle East
Neural network
Neural networks
power generation
prediction
Search algorithms
Spain
time series analysis
wavelet
Wavelet transform
Wavelet transforms
Wind power
Wind power generation
Wind speed
Wind speed forecasting
Middle East
Iran
Spain
Feature selection
Crow search algorithm
Wavelet transform
Neural network
Long short term memory
Wind speed forecasting
ISSN:0196-8904, 1879-2227
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Shrnutí:•Improve the accuracy and speed of the short term wind speed forecasting by a hybrid forecasting model.•Propose a feature selection method based on entropy and mutual information technique.•Implement a deep learning time series prediction model based on LSTM.•Using the Wavelet Transform module eliminate fluctuation behaviors of wind speed.•Using the Crow Search Algorithm to optimize the LSTM structure and the number of input features. 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.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2020.112824