Wind speed prediction based on nested shared weight long short-term memory network

doi:10.19682/j.cnki.1005-8885.2021.0004

The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (1): 41-51.doi: 10.19682/j.cnki.1005-8885.2021.0004

• Artificial intelligence • Previous Articles Next Articles

Wind speed prediction based on nested shared weight long short-term memory network

School of Computer and Communication Engineering, Qinhuangdao Branch of Northeast University, Qinhuangdao 066000, China

Received:2020-05-12 Revised:2020-08-09 Online:2021-02-28 Published:2021-03-28
Contact: Ying-Hua wuHAN E-mail:yhhan723@126.com
Supported by:
This work was supported by the National Key Research and Development Programe of China ( 2016YFB0901900 ), the National Natural Science Foundation of China (U1908213), the Fundamental the Research Funds for the Central Universities (N182303037), Colleges and Universities in Hebei Province Science Research Program ( QN2020504), the Foundation of

Northeastern University at Qinhuangdao (XNB201803).

Abstract

Abstract:

With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory (NSWLSTM) network and Gaussian process regression (GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory (LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR (NSWLSTMGPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.

Key words: wind speed prediction| feature extraction| long short-term memory network (LSTM)| shared weight| forecast uncertainty.

Han Fengquan, Han Yinghua , Lu Jing, Zhao Qiang. Wind speed prediction based on nested shared weight long short-term memory network[J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(1): 41-51.

References

1. Al-falahi M D A, Jayasinghe S D G, Enshaei H. A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Conversion and Management, 2017, 143: 252-274.

2. Zhang Y C, Liu K P, Qin L, et al. Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods. Energy Conversion and Management, 2016, 112: 208-219.

3. Liang Z T, Liang J, Wang C F, et al. Short-term wind power combined forecasting based on error forecast correction. Energy Conversion and Management, 2016, 119: 215-226.

4. Bouzgou H, Benoudjit N. Multiple architecture system for wind speed prediction. Applied Energy, 2011, 88(7): 2463-2471.

5. Peng X S, Xiong L, Wen J Y, et al. A summary of improved methods for short-term and ultra-short-term power forecasting accuracy of wind power clusters. Proceedings of the CSEE, 2016, 36(23): 6315-6326 (in Chinese)

6. Jung J, Broadwater R P. Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews, 2014, 31(2): 762-777.

7. Qian Z, Pei Y, Cao L J, et al. Review of wind power forecasting methods. High Voltage Technology, 2016, 42(4):1047-1060 (in Chinese).

8. Allen D J, Tomlin A S , Bale C S E , et al. A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data. Applied Energy, 2017, 208: 1246-1257.

9. Dong L, Wang L J, Khahro S F, et al. Wind power day-ahead prediction with cluster analysis of NWP. Renewable and Sustainable Energy Reviews, 2016, 60: 1206-1212.

10. Erdem E, Shi J. ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy, 2011, 88(4): 1405-1414.

11. Poncela M, Poncela P, Perán J R. Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting. Applied Energy, 2013, 108: 349-362.

12. Zhang C, Wei H K, Zhao X, et al. A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management, 2016, 126: 1084-1092.

13. Khosravi A, Koury R N N, Machado L, et al. Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustainable Energy Technologies and Assessments, 2018, 25: 146-160.

14. Noorollahi Y, Jokar M A , Kalhor A. Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Conversion and Management, 2016, 115: 17-25.

15. Liu H, Tian H Q, Pan D F, et al. Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks. Applied Energy, 2013, 107: 191-208.

16. Santamaría-Bonfil G, Reyes-Ballesteros A, Gershenson C. Wind speed forecasting for wind farms: A method based on support vector regression. Renewable Energy, 2015, 85: 790-809.

17. Wang L L, Li X, Bai Y L. Short-term wind speed prediction using an extreme learning machine model with error correction. Energy Conversion and Management, 2018, 162: 239-250.

18. Mi X W, Liu H, Li Y F. Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm. Energy Conversion and Management, 2017, 151: 709-722.

19. Zhang Z D, Ye L, Qin H, et al. Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression. Applied Energy, 2019, 247: 270-284.

20. Zheng W Q, Peng X G, Lu D, et al. Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach. Energy Conversion and Management, 2017, 151: 737-752.

21. Shi Z H, Liang H , Dinavahi V . Direct interval forecast of uncertain wind power based on recurrent neural networks. IEEE Transactions on Sustainable Energy, 2018, 9(3): 1177-1187.

22. Barbounis T G, Theocharis J B, Alexiadis M C, et al. Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion, 2006, 21(1): 273-284.

23. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8):1735-1780.

24. Liu H, Mi X W, Li Y F. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Conversion and Management, 2018, 159: 54-64.

25. Greff K, Srivastava R K, Koutník J, et al. LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 2015, 28(10): 2222-2232.

26. Moniz J R A, Krueger D. Nested LSTMs. Proceedings of the 9th Asian Conference on Machine Learning (ACML‘17), 2017, Nov 15-17, Seoul, Republic of Korea. New York, NY, USA: ACM, 2017: 530-544.

27. Kersting K, Plagemann C, Pfaff P, et al. Most-likely heteroscedastic Gaussian process regression. Proceedings of the 24th International Conference on Machine Learning (ICML’07), 2007, Jun 20-24, Corvallis, OR, USA. New York, NY, USA: ACM, 2007: 393-400.

28. Quinino R C, Reis E A, Bessegato L F. Using the coefficient of determination R2 to test the significance of multiple linear regression. Teaching Statistics, 2012, 35(2):84-88.

29. Li R R, Jin Y. A wind speed interval prediction system based on multi-objective optimization for machine learning method. Applied Energy, 2018, 228: 2207-2220.

30. Alessandrini S, Delle Monache L, Sperati S, et al. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 2015, 157: 95-110.

31. Liu Y Q, Ye L, Qin H, et al. Monthly streamflow forecasting based on hidden Markov model and Gaussian mixture regression. Journal of Hydrology, 2018, 561: 149-159.

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

[1]	Cheng Yi, Zhao Yan, Yin Peiwen. Radar false alarm plots elimination based on multi-feature extraction and classification [J]. The Journal of China Universities of Posts and Telecommunications, 2024, 31(1): 83-92.
[2]	Fan Xinyue, Wu Kai, Chen Shuai. RB-SLAM: visual SLAM based on rotated BEBLID feature point description [J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(3): 1-13.
[3]	. Hyperspectral remote sensing images terrain classification in DCT SRDA subspace [J]. Acta Metallurgica Sinica(English letters), 2015, 22(1): 65-71.
[4]	HUANG Hao; ZHU Jie. Discriminative tonal feature extraction method in mandarin speech recognition [J]. Acta Metallurgica Sinica(English letters), 2007, 14(4): 126-130.

Wind speed prediction based on nested shared weight long short-term memory network

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 4

Recommended Articles

Metrics

Comments