论文信息 - Multi-step forecasting of wave power using a nonlinear recurrent neural network

Multi-step forecasting of wave power using a nonlinear recurrent neural network

Short term forecasting is a vital interest to future implementations of a smart grid, particularly in the reliable integration of renewable energy resources. In this study we focus on multi-step prediction of high resolution wave power. Significant wave height data was first obtained from Belmullet Berth, Ireland and underwent several data preprocessing steps. These include a linear interpolation to fill irregular or missing data points, conversion to power using an interpolated power matrix of a Pelamis Device energy converter, and then exponential smoothing is applied. We utilized a nonlinear autoregressive recurrent neural network for 3, 6, 12 and 24 hour prediction. Our method showed highly accurate results when data has been smoothed, versus raw data, and when compared to previous studies.

[1] Francesco Fusco,et al. Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters , 2010, IEEE Transactions on Sustainable Energy.

[2] Richard J. Duro,et al. Discrete-time backpropagation for training synaptic delay-based artificial neural networks , 1999, IEEE Trans. Neural Networks.

[3] Hava T. Siegelmann,et al. Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[4] Gordon Reikard. Integrating wave energy into the power grid: Simulation and forecasting , 2013 .

[5] T. Chow,et al. Nonlinear autoregressive integrated neural network model for short-term load forecasting , 1996 .

[6] Peter A. E. M. Janssen,et al. Progress in ocean wave forecasting , 2008, J. Comput. Phys..

[7] Rob J Hyndman,et al. 25 years of time series forecasting , 2006 .

[8] H. Brian Hwarng,et al. Insights into neural-network forecasting of time series corresponding to ARMA(p,q) structures , 2001 .

[9] S. Mandal,et al. Ocean wave forecasting using recurrent neural networks , 2006 .

[10] M. C. Deo,et al. Real-time wave forecasting using genetic programming , 2008 .

[11] Robert Tibshirani,et al. A Comparison of Some Error Estimates for Neural Network Models , 1996, Neural Computation.

[12] Everette S. Gardner,et al. Exponential smoothing: The state of the art , 1985 .

[13] Makarand Deo,et al. Prediction of breaking waves with neural networks , 2003 .