Short term prediction of ocean wave energy power using long-short term memory network

NI Chenhua

doi:10.3981/j.issn.1000-7857.2021.06.008

PDF(1940 KB)

Science & Technology Review ›› 2021, Vol. 39 ›› Issue (6) : 59-65. DOI: 10.3981/j.issn.1000-7857.2021.06.008

Exclusive: Ocean Energy Development

Short term prediction of ocean wave energy power using long-short term memory network

NI Chenhua

Author information -

National Ocean Technology Center, Tianjin 300112, China

Abstract

The prediction technologies of the power generation from the wave energy converters (WEC) are an urgent and crucial problem in the renewable energy planning, the power grid dispatching and the economic operation. Besides the statistical modelling, this paper presents a novel hybrid DDM for very short term (15 min-4 h) and short term (0-72 h) predictions of the wave energy power, based on the long-short term memory (LSTM) network and the results are compared with those obtained by the Artificial neural networks (ANN) and the support vector machine. The experimental results indicate that the proposed deep learning models enjoy a better performance with a high accuracy in the WEC power prediction than other related models. Furthermore, the proposed DDM methods are shown to be robust and timesaving in training and deployment, with advantages over the statistical methods in very short term and short term WEC power predictions.

Key words

short-term prediction / wave energy converter / data-driven modelling / long-short term memory

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

NI Chenhua. Short term prediction of ocean wave energy power using long-short term memory network[J]. Science & Technology Review, 2021, 39(6): 59-65 https://doi.org/10.3981/j.issn.1000-7857.2021.06.008

References

[1] 郭剑波. 高比例新能源电力系统的挑战[C]//2020中国可再生能源学术大会. 昆明:中国可再生能源学会, 2020:8-10.
[2] 王桓, 徐龙博. 风电功率预测技术与实例分析[M]. 北京:中国水利水电出版社, 2016:34-38.
[3] 吴必军, 邓赞高, 游亚戈. 基于波浪能的蓄能稳压独立发电系统仿真[J]. 电力系统自动化, 2007, 31(5):50-56.
[4] 刘延俊. 波浪能发电装置设计与制造[M]. 北京:化学工业出版社, 2019:66-69.
[5] Phillips O M. On the generation of waves by a turbulent wind[J]. Journal of Fluid Mechanics, 1957, 2:417-445.
[6] Phillips O M. The equilibrium range in the spectrum of wind-generated waves[J]. Journal of Fluid Mechanics, 1958, 4:426-434.
[7] Tolman T L. User manual and system documentation of WAVEWATCH-III version 2.22[R/OL].[2020-12-05]. http://polar.ncep.noaa.gov/waves/wavewatch/manual.v4.18.pdf.
[8] Reikard G, Robertson B, Bidlot J R. Combining wave energy with wind and solar:Short-term forecasting[J]. Renewable Energy, 2015, 81:442-456.
[9] Surf's up:Professor using models to predict huge waves[R/OL].[2005-2-23]. ScienceDaily, https://www.sciencedaily.com/releases/2005/02/050222193810.htm.
[10] Liu H, Erdem E, Shi J. Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed[J]. Applied Energy, 2011, 88(3):724-732.
[11] Helga S, Claudiu C. Wind speed prediction using Box-Jenkins method[J]. Journal of Computer Science & Control Systems, 2008(1):208-212.
[12] Poncela M, Poncela P, Perán J R. Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting[J]. Applied Energy, 2013, 108(12):349-362.
[13] Song Z, Jiang Y, Zhang Z. Short-term wind speed forecasting with Markov-switching model[J]. Applied Energy, 2014, 130:103-112.
[14] Peres D J, Iuppa C, Cavallaro L, et al. Significant wave height record extension by neural networks and reanalysis wind data[J]. Ocean Modelling, 2015, 94:128-140.
[15] Reikard G, Pinson P, Bidlot J R. Forecasting ocean wave energy:The ECMWF wave model and time series methods[J]. Ocean Engineering, 2011, 38(10):1089-1099.
[16] Kumar N K, Savitha R, Al Mamun A. Regional ocean wave height prediction using sequential learning neural networks[J]. Ocean Engineering, 2017, 129:605-612.
[17] Wang H Z, Li G Q, Wang G B, et al. Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 2017, 188:56-70.
[18] Liu Y, Guan L, Hou C, et al. Wind power short-term prediction based on LSTM and discrete wavelet transform[J]. Applied Sciences, 2019, 9(6):1108.
[19] 陈仲铭, 彭凌西. 深度学习原理与实践[M]. 北京:人民邮电出版社, 2018:268-270.
[20] Sepp H, Jürgen S. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[21] Yan X, Wang Y, Du N, et al. Multi-step short-term power consumption forecasting with a hybrid deep learning strategy[J]. Energies, 2018, 11:3089.
[22] ElSaid A, Wildy B, Higginsy J, et al. Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines[C]//IEEE 12th International Conference on e-Science. Piscataway NJ:IEEE, 2016:260-269.
[23] Zhao Z, Chen W, Wu X, et al. LSTM network:A deep learning approach for short-term traffic forecast[J]. IET Intelligent Transport Systems, 2017, 11(2):68-75.
[24] Kiperwasser E, Goldberg Y. Simple and accurate dependency parsing using bidirectional LSTM feature representations[J]. Transactions of the Association for Computational Linguistics, 2016, 4:313-327.
[25] 吴岸城. 神经网络与深度学习[M]. 北京:电子工业出版社, 2016:67-68.