Wind speed prediction with RBF neural network based on PCA and ICA

Open access


Thanks to non-pollution and sustainability of wind energy, it has become the main source of power generation in the new era worldwide. However, the inherent random fluctuation and intermittency of wind power have negative effects on the safe and stable operation of power system and the quality of power. The key solving this problem is to improve the accuracy of wind speed prediction. In the paper, considering the forecasting accuracy is affected by many factors, we propose that, Principal Component Analysis (PCA) is combined with Independent Component Analysis (ICA) to process the sample, which can weaken the mutual interference between the various factors, extract accurately independent component reflected the characteristics of wind farm and achieve the purpose of improving the accuracy of wind speed prediction. At the same time, the adaptive and self-learning ability of neural network is more suitable for wind speed sequence prediction. The prediction results demonstrate that compared with the traditional neural network predicting model (RBF, BP, Elman), this model makes full use of the information provided by varieties of relevant factors, weakens the volatility of wind speed sequence and significantly enhances the short-term wind speed forecasting accuracy. The research work in the paper can help wind farm reasonably arrange the power dispatching plan, reduce the power operation cost and effectively boost the large-scale development and utilization of renewable energy.

[1] “Global wind energy council” (GWEC) [DB/OL].

[2] E. T. Renani, M. F Mohamad Elias and N. A. Rahim, “Using data-driven approach for wind power prediction: A comparative study”, Energy Conversion and Management, vol. 118, pp. 193-203.June.2016.doi: 10.1016/j.enconman.2016.03.078.

[3] A. Khosravi, R. N. N. Koury, L. Machado and J. J. G. Pabon, “Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system”, Sustainable Energy Technologies and Assessments, vol. 25, pp. 146-160, February 2018.doi: 10.1016/j.seta.2018.01.001.

[4] S. X. Zhang, Z. P. Zhou, X. M. Chen, Y. Hu and L. D. Yang,”PDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine”, Journal of Theoretical Biology, vol. 426, pp. 126-133, August 2017.doi: 10.1016/j.jtbi.2017.05.030.

[5] F. Bre and J. M. Gimenez, “Prediction of wind pressure coefficients on building surfaces using artificial neural networks”, Energy and Buildings, vol. 158, pp. 1429-1441, January 2018.doi: 10.1016/j.enbuild.2017.11.045.

[6] J. P. Jeon, C. Kim, B. D. Oh and S. J. Kim, “Prediction of persistent hemodynamic depression after carotid angioplasty and stenting artificial neural network model”, Clinical Neurology and Neurosurgery, vol. 164, pp. 127-131, December 2017.doi: 10.1016/j.clineuro.2017.12.005.

[7] P. Ramasamy, S. S. Chandel and A. K. Yadav, “Wind speed prediction the mountainous region of India using an artificial neural network model”, Applied Energy, vol. 80, pp. 338-347, August 2015.doi: 10.1016/j.renene.2015.02.034.

[8] V. Prema and K. Uma Rao, “Development of statistical time series models for solar power prediction”, Renewable Energy, vol. 83, pp. 100-109, November 2015.doi: 10.1016/j.renene.2015.03.038.

[9] Y. N. Zhao, L. Ye, Z. Li, X. R. Song and Y. S. Lang, “A novel bidirectional mechanism based on time series model for wind power forecasting”, Applied Energy. vol. 177, pp. 793-803, Mar.2016.doi: 10.1016/j.apenergy.2016.03.096.

[10] Y. G. Zhang, P. H. Wang, P. L. Cheng, and S. Lei, “Wind speed prediction with wavelet time series based on Lorenz disturbance”, Advances Electrical and Computer Engineering, vol. 17, pp. 107-114, August 2017.doi: 10.1016/j.aece.2017.03.014.

[11] R. Rajesh, “Forecasting supply chain resilience performance using grey prediction”, Electronic Commerce Research and Applications , vol. 20, pp. 42-58, sep.2016.doi: 10.1016/j.elerap.2016.09.006.

[12] A. Bezuglov and G. Comert, “Short-term freeway traffic parameter prediction: Application of grey system theory models”, Expert Systems with Application, vol. 62, pp. 284-292, November 2016.doi: 10.1016/j.eswa.2016.06.032.

[13] A. Jackson and B. Turnbull, “Identification of particle-laden flow features from wavelet decomposition”, Physica D: Nonlinear Phenomena, vol. 361, pp. 12-27, December 2016.doi: 10.1016/j.physd.2017.09.009.

[14] K. Y. Zhang and R. Gencay and M. E. Yazgan, “Application of wavelet decomposition time series forecasting”, Economics Letters , vol. 158, pp. 41-46, Sep.2016.doi: 10.1016/j.enconlet.2017.06.010.

[15] S.W. Fei and Y. He “Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine”, International Journal of Electrical Power & Energy Systems, vol. 73, pp. 625-631, December 2015, doi:10.1016/j.ijepes.2015.04.019.

[16] V. Gupta and M. Mittal, “KNN and PCA classifier with Autoregressive modelling during different ECG signal interpretation”, Procedia Computer Science, vol. 125, pp. 18-24, December 2017.doi: 10.1016/j.procs.2017.12.005.

[17] E. P. Duff, A. J. Trachtenberg, C. E. Mackay, M. A. Howard, F. Wilson, S. M. Smith and M. W. Woolrich, “Task-driven ICA feature generation for accurate and interpretable prediction using fMRI”, NeuroImage, vol. 60, pp. 189-203, Mar.2016.doi: 10.1016/j.procs.2011.12.053.

[18] J. Naik, P. Satapathy and P. K. Dash, “Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression”, Applied Soft Computing, pp. ASOC-4606, Dec. 2017, doi: 10.1016/j.asoc.2017.12.010.

[19] H. Zhang, A. Palazoglu, X. Y. Zhang, W. D. Zhang, Z. M. Zhao andW. S. S.W. L., “Prediction of surface ozone exceedance days using PCA with a non-parametric control limit”, Chemometrics and Intelligent Laboratory Systems, vol. 133 pp. 42-48, April 2014.doi: 10.1016/j.chemolab.2014.02.005.

[20] S. J. Dong and A. T. H. Luo, “Bearing degradation process prediction based on the PCA and optimized LS-SVM model”, Measurement, vol. 46 pp. 3143-3152, November 2013.doi: 10.1016/j.mearsurement.2013.02.005.

[21] A. Datteo, F. Luca and G. Busca, “Statistical pattern recognition approach for long-time monitoring of the G. Meazza stadium by means of AR models and PCA”, Engineering Structures, vol. 153 pp. 317-333, Dec. 2017, doi: 10.1016/j.engstruct.2017.10.022.

[22] J. C. Pereira, J. C. R. Azevedo, H. G. Knapik and H. D. Burrows, “Unsupervised component analysis: PCA, POA, ICA data exploring-connecting the dots”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 165 pp. 69-84, August 2016.doi: 10.1016/j.saa.2016.03.48.

[23] Y. G. Zhang, P. H. Wang, T. Ni, P. L. Cheng and Shuang Lei, “Wind power prediction based on LS-SVM model with error correction”, Advances Electrical and Computer Engineering, vol. 17, pp. 3-8, January 2017.doi: 10.1016/j.aece.2017.01.00.

[24] N. Kwak, C. Kim and H. Kim, “Dimensionality reduction based on ICA for regression problems”, Neurocomputing, vol. 71 pp. 2596-2603, August 2016.doi: 10.1016/j.neucom.2007.11.036.

[25] R. J. Martis, U. R. Acharya and L.C. Min, “ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform”, Biomedical Signal Processing and Control, vol. 8 pp. 437-448, Sep.2013.doi: 10.1016/j.bspc.2013.01.005.

[26] Y. G. Zhang, J. Y. Yang, K. C. Wang, Z. P. Wang and Y. D. Wang, “Improved wind prediction based on the Lorenz system”, Renewable Energy, vol. 81, pp. 219-226, Mar.2015.doi: 10.1016/j.renene.2015.03.039.

Journal of Electrical Engineering

The Journal of Slovak University of Technology

Journal Information

IMPACT FACTOR 2017: 0.508
5-year IMPACT FACTOR: 0.549

CiteScore 2017: 0.78

SCImago Journal Rank (SJR) 2017: 0.205
Source Normalized Impact per Paper (SNIP) 2017: 0.506

Cited By


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 386 386 35
PDF Downloads 275 275 21