A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets - Memarzadeh, Gholamreza; Keynia, Farshid (Energy conversion and management, 2020)
•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.