Very short-term wind power forecasting with neural networks and adaptive Bayesian learning

This article presents an adaptive very short-term wind power prediction scheme that uses an artificial neural network as predictor along with adaptive Bayesian learning and Gaussian process approximation. A set of recent wind speed measurements samples composes the predictor’s inputs. The predictor’s parameters are adaptively optimized so that, at a given time t, its outputs approximate the future values of the generated electrical power. An evaluation of this prediction scheme was conducted for two tests cases; the predictor was set to simultaneously estimate the values of the wind power for the following prediction horizons: 5min, 10min and 15min for test case n°1 and for the test case n°2, the prediction horizons were 10min, 20min and 30min. The neural predictor performs better than the persistent model for both test cases. Moreover, the Bayesian framework also permits to predict, for a specified level of probability, the interval within which the generated power should be observed.

[1]  Tomonobu Senjyu,et al.  A new strategy for predicting short-term wind speed using soft computing models , 2012 .

[2]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[3]  Mohammad Monfared,et al.  A new strategy for wind speed forecasting using artificial intelligent methods , 2009 .

[4]  D. Mackay,et al.  Bayesian methods for adaptive models , 1992 .

[5]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[6]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[7]  A. Celik.  Weibull representative compressed wind speed data for energy and performance calculations of wind energy systems , 2003 .

[8]  Pavlos S. Georgilakis,et al.  Technical challenges associated with the integration of wind power into power systems , 2008 .

[9]  Erik Lundtang Petersen.  Contributions to the European Wind Energy Conference and Exhibition, London, 2004 , 2006 .

[10]  P. Dokopoulos,et al.  Short-term forecasting of wind speed and related electrical power , 1998 .

[11]  Pierre Pinson,et al.  Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models , 2005 .

[12]  Gregor Giebel,et al.  State-of-the-Art on Methods and Software Tools for Short-Term Prediction of Wind Energy Production , 2003 .

[13]  Gomes de Freitas,et al.  Bayesian methods for neural networks , 2000 .

[14]  A. Louche,et al.  Forecasting and simulating wind speed in Corsica by using an autoregressive model , 2003 .

[15]  Rodolfo Pallabazzer,et al.  Previsional estimation of the energy output of windgenerators , 2004 .

[16]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .