DOI: 10.3390/en8010475. Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

10.3390/en8010475

Crossref journal-article

MDPI AG

Energies (1968)

Abstract

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.

Bibliography

Zhang, Y., Yang, J., Wang, K., & Wang, Z. (2015). Wind Power Prediction Considering Nonlinear Atmospheric Disturbances. Energies, 8(1), 475â489.

Authors 4

Yagang Zhang (first)
Jingyun Yang (additional)
Kangcheng Wang (additional)
Zengping Wang (additional)

References 27 Referenced 21

Global Wind Energy Council (GWEC). Available online: http://www.gwec.net/global-figures/graphs/.
10.1016/j.renene.2013.05.012 / Renew. Energy / A hybrid forecasting approach applied to wind speed time series by Hu (2013)
10.1016/j.asoc.2013.02.016 / Appl. Soft. Comput. / Short-term wind speed forecasting based on a hybrid model by Zhang (2013)
10.1016/j.egypro.2011.10.103 / Energy Proced. / A review of wind power forecasting models by Wang (2011)
10.1016/j.renene.2011.05.033 / Renew. Energy / Current methods and advances in forecasting of wind power generation by Foley (2011)
Chen, Z.H., Xu, Y., Xu, P.H., Yang, H.Q., and Xiong, S.Q. (2013). Principle of Wind Power Prediction Technology and Business Systems, China Meteorological Press. [1st ed.].
10.1016/j.apenergy.2011.01.037 / Appl. Energy / Multiple architecture system for wind speed prediction by Bouzgou (2011)
10.1016/j.enconman.2013.01.033 / Energy Convers. Manag. / A new approach to very short term wind speed prediction using k-nearest neighbor classification by Yesilbudak (2013)
10.1016/j.renene.2009.12.011 / Renew. Energy / A hybrid statistical method to predict wind speed and wind power by Liu (2010)
10.1016/j.rser.2014.03.033 / Renew. Sustain. Energy Rev. / A review of combined approaches for prediction of short-term wind speed and power by Tascikaraoglu (2014)
10.1016/j.jweia.2012.07.002 / J. Wind Eng. Ind. Aerodyn. / The effect of different state sizes on Mycielski approach for wind speed prediction by Gan (2012)
10.1016/j.protcy.2013.04.041 / Proced. Technol. / Wavelet neural networks for predicting engine emissions by Palacios (2013)
10.1016/j.enconman.2010.11.007 / Energy Convers. Manag. / Fine tuning support vector machines for short-term wind speed forecasting by Zhou (2011)
10.1016/j.renene.2008.04.017 / Renew. Energy / A new strategy for wind speed forecasting using artificial intelligent methods by Monfared (2008)
10.1016/j.apenergy.2012.04.001 / Appl. Energy / Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction by Liu (2010)
10.1016/j.neucom.2013.06.008 / Neurocomputing / Neural network based hybrid computing model for wind speed prediction by Sheela (2013)
10.1016/j.apenergy.2013.02.002 / Appl. Energy / Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks by Liu (2013)
10.1016/j.rser.2014.01.033 / Renew. Sustain. Energy Rev. / Review on probabilistic forecasting of wind power generation by Zhang (2014)
10.1016/j.enconman.2014.04.028 / Energy Convers. Manag. / A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm by Guo (2014)
10.3390/en7117178 / Energies / Lorenz wind disturbance model based on grey generated components by Zhang (2014)
Liu, B.Z., and Peng, J.H. (2007). Nonlinear Dynamics, Higher Education Press. [1st ed.].
Lorenz, E.N. (2008). The Butterfly Effect, Premio Felice Pietro Chisesi e Caterina Tomassoni award lecture; University of Rome.
10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2 / J. Atmos. Sci. / Deterministic nonperiodic flow by Lorenz (1963)
10.1016/j.physd.2013.06.008 / Phys. D Nonlinear Phenom. / Comments on ‘Global dynamics of the generalized Lorenz systems having invariant algebraic surfaces’ by Algaba (2014)
10.1016/j.scient.2010.11.001 / Sci. Iran. D / Complex dynamic behaviors of the complex Lorenz system by Moghtadaei (2012)
Lorenz, E.N. (1997). The Essence of Chaos, China Meteorological Press. [1st ed.].
10.1175/1520-0469(1962)019<0329:FAFCAA>2.0.CO;2 / J. Atmos. Sci. / Finite amplitude free convection as an initial value problem-I by Saltzman (1962)

Dates

Type	When
Created	10 years, 7 months ago (Jan. 13, 2015, 11:28 a.m.)
Deposited	1 year, 2 months ago (June 3, 2024, 8:36 a.m.)
Indexed	3 months ago (May 18, 2025, 7:41 p.m.)
Issued	10 years, 7 months ago (Jan. 13, 2015)
Published	10 years, 7 months ago (Jan. 13, 2015)
Published Online	10 years, 7 months ago (Jan. 13, 2015)

Funders 0

None

BibTeX

@article{Zhang_2015, title={Wind Power Prediction Considering Nonlinear Atmospheric Disturbances}, volume={8}, ISSN={1996-1073}, url={http://dx.doi.org/10.3390/en8010475}, DOI={10.3390/en8010475}, number={1}, journal={Energies}, publisher={MDPI AG}, author={Zhang, Yagang and Yang, Jingyun and Wang, Kangcheng and Wang, Zengping}, year={2015}, month=jan, pages={475–489} }

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