New Progress in Wind Prediction Based on Nonlinear Amendment

In recent years, large-scale wind power integration on electric system gradually becomes to be a major trend to the development of wind power industry. Thus, high precision wind speed and power prediction technology is urgently needed. Being different from traditional wind prediction models that largely rely on various numerical methods, this paper considers the dynamical essence features of atmospheric motion. A brand new Lorenz disturbance prediction model, which is based on wavelet neural networks (WNN), is proposed and called LSWNN short-term wind speed prediction model. Compared with the results of WNN model, LSWNN model is more accurate for the actual wind speed distribution forecasting. In this article, the research not only has important theoretical value on analyzing atmospheric nonlinear motion process, but also has profound engineering guidance in wind speed prediction and wind energy resource exploitation.

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

[2]  Wenyu Zhang,et al.  Short-term wind speed forecasting based on a hybrid model , 2013, Appl. Soft Comput..

[3]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[4]  Seref Sagiroglu,et al.  A new approach to very short term wind speed prediction using k-nearest neighbor classification , 2013 .

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

[6]  K. Gnana Sheela,et al.  Neural network based hybrid computing model for wind speed prediction , 2013, Neurocomputing.

[7]  Shouyang Wang,et al.  Forecasting stock market movement direction with support vector machine , 2005, Comput. Oper. Res..

[8]  Chao Chen,et al.  A hybrid statistical method to predict wind speed and wind power , 2010 .

[9]  Y. Zhang,et al.  A Novel Approach to Fault Detection in Complex Electric Power Systems , 2014 .

[10]  Farshid Keynia,et al.  Short-term wind power forecasting using ridgelet neural network , 2011 .

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

[12]  Ashu Jain,et al.  Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..

[13]  Xiaofeng Yang,et al.  A hybrid strategy of short term wind power prediction , 2013 .

[14]  Ming Ding,et al.  The effect of different state sizes on Mycielski approach for wind speed prediction , 2012 .

[15]  Zengping Wang,et al.  Bifurcation criterion of faults in complex nonlinear systems , 2014 .

[16]  A. Alessandri,et al.  Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models , 2013 .

[17]  Zengping Wang,et al.  Fault Factor Analysis in Complicated Electrical Engineering , 2013 .

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