Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance.

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

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

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

[4]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[5]  Akin Tascikaraoglu,et al.  A review of combined approaches for prediction of short-term wind speed and power , 2014 .

[6]  Alejandro J. Rodríguez-Luis,et al.  Comments on ‘Global dynamics of the generalized Lorenz systems having invariant algebraic surfaces’ , 2014 .

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

[8]  E. Lorenz.  Deterministic nonperiodic flow , 1963 .

[9]  Yagang Zhang,et al.  Lorenz Wind Disturbance Model Based on Grey Generated Components , 2014 .

[10]  Edward N. Lorenz.  The Butterfly Effect , 2000 .

[11]  Jianxue Wang,et al.  Review on probabilistic forecasting of wind power generation , 2014 .

[12]  Nabil Benoudjit,et al.  Multiple architecture system for wind speed prediction , 2011 .

[13]  C. E. Puente,et al.  The Essence of Chaos , 1995 .

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

[15]  Zhenhai Guo,et al.  A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm , 2014 .

[16]  José D. Martínez-Morales,et al.  Wavelet Neural Networks for Predicting Engine Emissions , 2013 .

[17]  Peng Guo,et al.  A Review of Wind Power Forecasting Models , 2011 .

[18]  Hui Liu,et al.  Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction , 2012 .

[19]  Jianzhou Wang,et al.  A hybrid forecasting approach applied to wind speed time series , 2013 .

[20]  Motahareh Moghtadaei,et al.  Complex dynamic behaviors of the complex Lorenz system , 2012, Sci. Iran..

[21]  Jing Shi,et al.  Fine tuning support vector machines for short-term wind speed forecasting , 2011 .

[22]  N. MacDonald.  Nonlinear dynamics , 1980, Nature.

[23]  Barry Saltzman,et al.  Finite Amplitude Free Convection as an Initial Value Problem—I , 1962 .

[24]  Hui Liu,et al.  Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks , 2013 .

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