Error analysis of short term wind power prediction models

The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction.

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

[2]  M. Negnevitsky,et al.  Short term wind power forecasting using adaptive neuro-fuzzy inference systems , 2007, 2007 Australasian Universities Power Engineering Conference.

[3]  J. C. Quadrado,et al.  Wind speed prediction using artificial neural networks , 2005 .

[4]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[5]  A. Tapia,et al.  Application of a control algorithm for wind speed prediction and active power generation , 2005 .

[6]  Ioannis B. Theocharis,et al.  Locally recurrent neural networks for long-term wind speed and power prediction , 2006, Neurocomputing.

[7]  Athanasios Sfetsos,et al.  A novel approach for the forecasting of mean hourly wind speed time series , 2002 .

[8]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[9]  D. Fadare.  The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria , 2010 .

[10]  Ignacio J. Ramirez-Rosado,et al.  Comparison of two new short-term wind-power forecasting systems , 2009 .

[11]  Fawzi A. L. Jowder,et al.  Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain , 2009 .

[12]  Erik Delarue,et al.  Considerations on the backup of wind power: Operational backup , 2007 .

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

[14]  Antonio J. Conejo,et al.  A methodology to generate statistically dependent wind speed scenarios , 2010 .

[15]  G. Cirrincione,et al.  Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor , 2010 .

[16]  Zhou Wei,et al.  Optimal design and techno-economic analysis of a hybrid solar–wind power generation system , 2009 .

[17]  Mehrdad Abedi,et al.  Short term wind speed forecasting for wind turbine applications using linear prediction method , 2008 .

[18]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[19]  Christopher J. Koroneos,et al.  Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece , 2009 .

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

[21]  W. Rivera,et al.  Wind speed forecasting in the South Coast of Oaxaca, México , 2007 .

[22]  George Stavrakakis,et al.  Advanced short-term forecasting of wind power production , 1997 .

[23]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[24]  M. Bilgili,et al.  Application of artificial neural networks for the wind speed prediction of target station using reference stations data , 2007 .

[25]  Wei Zhou,et al.  Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems , 2010 .

[26]  Michael Negnevitsky,et al.  SHORT-TERM WIND FORECASTING TECHNIQUES FOR POWER GENERATION , 2004 .

[27]  D. Sailor,et al.  A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change , 2000 .