Abstract
Wind speed has an important impact on the navigation of ships at sea. If the wind speed can be accurately predicted, the safety of ship navigation would be greatly improved. This paper proposes a wind speed series prediction model based on SARIMA and LSTM. Firstly, the SARIMA model is used to predict and model the observed wind speed sequence data to obtain the predicted value and the residual between the predicted value and the observed value. Training the long and short-term memory neural network with the residual sample set to get a trained network for residual prediction. Finally, to sum the two parts predicted values up to obtain the predicted value of the wind speed series. In order to test the prediction effect of this model, a deep learning environment based on Keras was built, and 5 days of real-time wind speed data in a certain sea area of the South China Sea was used as the input of the model. The prediction results are compared with the prediction results of SARIMA model, LSTM network model, BP network model, LSTM and ARIMA combined model. The experimental results show that the model has high accuracy and less error in the prediction of wind speed series.
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References
Ding, M., Zhang, L., Wu, Y.: Wind speed forecast model for wind farms based on time series analysis. Electric Pow. Autom. Equip. 25(8), 32–34 (2005)
Erdem, E., Shi, J.: ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 88(4), 1405–1414 (2011)
Zuluaga, C.D., Alvarez, M.A., Giraldo, E.: Short-term wind speed prediction based on robust Kalman filtering: an experimental comparison. Appl. Energy 156, 321–330 (2015)
Wang, L., Dong, L., Liao, X., Gao, Y.: Short-term power prediction of a wind farm based on wavelet analysis. Proc. CSEE 29(28), 30–33 (2009)
Abedinia, O., Lotfi, M., Bagheri, M., Sobhani, B., Shafie-Khah, M., Catalão, J.P.: Improved EMD-based complex prediction model for wind power forecasting. IEEE Trans. Sustain. Energy 11(4), 2790–2802 (2020)
Zendehboudi, A., Baseer, M., Saidur, R.: Application of support vector machine models for forecasting solar and wind energy resources: a review. J. Clean. Prod. 199, 272–285 (2018)
Santamaría-Bonfil, G., Reyes-Ballesteros, A., Gershenson, C.: Wind speed forecasting for wind farms: a method based on support vector regression. Renew. Energy 85, 790–809 (2016)
Alexiadis, M., Dokopoulos, P., Sahsamanoglou, H.: Wind speed and power forecasting based on spatial correlation models. IEEE Trans. Energy Convers. 14(3), 836–842 (1999)
Cao, Q., Ewing, B.T., Thompson, M.A.: Forecasting wind speed with recurrent neural networks. Eur. J. Oper. Res. 221(1), 148–154 (2012)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Mishra, S.P., Dash, P.K.: Short term wind speed prediction using multiple kernel pseudo inverse neural network. Int. J. Autom. Comput. 15(1), 66–83 (2017). https://doi.org/10.1007/s11633-017-1086-7
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Acknowledgments
This research is supported by National Key Research and Development Program of China under grant number 2017YFC1405404, and Green Industry Technology Leading Project (product development category) of Hubei University of Technology under grant number CPYF2017008.
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Xiong, C., Yu, C., Gu, X., Xu, S. (2021). Time Series Prediction of Wind Speed Based on SARIMA and LSTM. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_6
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DOI: https://doi.org/10.1007/978-3-030-79725-6_6
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