Volume 5 (2), December 2022, Pages 169-182

Navid Atashfaraz1, Mohammad Manthouri2, Arash Hosseini3

1North Tehran Branch Azad University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

2Shahed University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

3North Tehran Branch Azad University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.


Wind speed/power has received increasing attention worldwide due to its renewable nature and environmental friendliness. Wind power capacity is rapidly increasing with the global installed, and the wind industry is growing into a large-scale business. We are looking for wind speed prediction to use wind power better. In this research, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (Simple RNN), and LSTM-GRU in the subset of artificial intelligence algorithms are used to predict wind speed. The data used in this study are related to the 10-minute wind speed data. In the first study on this dataset, we obtained significant results. To compare the deep recurrent models created, we implement four neural network models: Stacked Auto Encoder, Denoising Auto Encoder, Stacked Denoising Auto Encoder, and Feed-Forward presented in the research of others on this dataset. According to the RMSE statistical index, the LSTM network is worth 0.0222 for a short time and performs better than others in this dataset.


Deep Learning, LSTM, GRU, RNN, Wind Speed Forecasting.





Cao, Q., Ewing, B. T., & Thompson, M. A. (2012). Forecasting wind speed with recurrent neural networks. European Journal of Operational Research, 221(1), 148-154.

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Eseye, A. T., Zhang, J., et al. (2017, March). A double-stage hierarchical ANFIS model for short-term wind power prediction. In 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA) (pp. 546-551). IEEE.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Hu, Y. L., & Chen, L. (2018). A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm. Energy conversion and management, 173, 123-142.

Kenarang, A., Farahani, M., & Manthouri, M. (2022). BiGRU attention capsule neural network for persian text classification. Journal of Ambient Intelligence and Humanized Computing, 1-11.

Khodayar, M., & Teshnehlab, M. (2015, September). Robust deep neural network for wind speed prediction. In 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) (pp. 1-5). IEEE.

Khodayar, M., Kaynak, O., & Khodayar, M. E. (2017). Rough deep neural architecture for short-term wind speed forecasting. IEEE Transactions on Industrial Informatics, 13(6), 2770-2779.

Khodayar, M., Wang, J., & Manthouri, M. (2018). Interval deep generative neural network for wind speed forecasting. IEEE Transactions on Smart Grid, 10(4), 3974-3989.

Kushwah, A. K., & Wadhvani, R. (2019). Performance monitoring of wind turbines using advanced statistical methods. Sādhanā, 44(7), 1-11.

Lee, D., & Baldick, R. (2013). Short-term wind power ensemble prediction based on Gaussian processes and neural networks. IEEE Transactions on Smart Grid, 5(1), 501-510.

Liu, H., Mi, X., & Li, Y. (2018). An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm. Renewable energy, 123, 694-705.

Liu, H., Tian, H. Q., et al. (2013). Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Applied Energy, 107, 191-208.

McCarthy, E. F. (1997). Wind speed forecasting in the central California wind resource area (No. CONF-970608-PROC.). American Wind Energy Association, Washington, DC (United States).

Philippopoulos, K., & Deligiorgi, D. (2012). Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography. Renewable Energy, 38(1), 75-82.

Qin, Y., Li, K., et al. (2019). Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal. Applied energy, 236, 262-272.

Sun, S., Qiao, H., Wei, Y., & Wang, S. (2017). A new dynamic integrated approach for wind speed forecasting. Applied energy, 197, 151-162.

Vinothkumar, T., & Deeba, K. (2020). Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models. Soft Computing, 24(7), 5345-5355.

Wu, Y. K., & Hong, J. S. (2007). A literature review of wind forecasting technology in the world. 2007 IEEE Lausanne Power Tech, 504-509.

Yang, L., He, M., Zhang, J., & Vittal, V. (2015). Support-vector-machine-enhanced markov model for short-term wind power forecast. IEEE Transactions on Sustainable Energy, 6(3), 791-799.