Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting

Ye Ren^*, P. N. Suganthan

School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore City, Singapore.

DOI: 10.4236/jpee.2014.24025                

Abstract

Hybrid model is a popular forecasting model in renewable energy related forecasting applications.
Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.

Keywords

Wind Speed Forecasting; Empirical Mode Decomposition; k Nearest Neighbor

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Wu, Y.K. and Hong, J.S. (2007) A Literature Review of Wind Forecasting Technology in the World. IEEE Lausanne Power Tech, Lausanne, 1-5 July 2007, 504-509.
[2]	Hill, D.C., McMillan, D., Bell, K.R.W. and Infield, D. (2012) Application of Auto-Regressive Models to U.K. Wind Speed Data for Power System Impact Studies. IEEE Transactions on Sustainable Energy, 3, 134-141. http://dx.doi.org/10.1109/TSTE.2011.2163324
[3]	Damousis, I., Alexiadis, M., Theocharis, J. and Dokopoulos P., (2004) A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks Using Spatial Correlation. IEEE Transactions on Energy Conversion, 19, 352-361. http://dx.doi.org/10.1109/TEC.2003.821865
[4]	Salcedo-Sanz, S., Ortiz-Garcia, E.G., Perez-Bellido, A.M., Portilla-Figueras, A. and Prieto, L. (2011) Short Term Wind Speed Prediction Based on Evolutionary Support Vector Regression Algorithms. Expert Systems with Applications, 38, 4052-4057. http://dx.doi.org/10.1016/j.eswa.2010.09.067
[5]	Shi, J., Guo, J. and Zheng, S. (2012) Evaluation of Hybrid Forecasting Approaches for Wind Speed and Power Generation Time Series. Renewable and Sustainable Energy Reviews, 16, 3471-3480. http://dx.doi.org/10.1016/j.rser.2012.02.044
[6]	Wang, Y., Niu, D. and Ma, X. (2010) Optimizing of SVM with Hybrid PSO and Genetic Algorithm in Power Load Forecasting. Journal of Networks, 5, 1192-1200. http://dx.doi.org/10.4304/jnw.5.10.1192-1200
[7]	Zeng, J. and Qiao, W. (2012) Short-Term Wind Power Prediction Using a Wavelet Support Vector Machine. IEEE Transactions on Sustainable Energy, 3, 255-264. http://dx.doi.org/10.1109/TSTE.2011.2180029
[8]	Guo, Z., Zhao, W., Lu, H. and Wang, J. (2012) Multi-Step Forecasting for Wind Speed Using a Modified EMD-Based Artificial Neural Network Model. Renewable Energy, Elsevier, 37, 241-249. http://dx.doi.org/10.1016/j.renene.2011.06.023
[9]	Catalao, J., Pousinho, H. and Mendes, V. (2011) Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal. IEEE Transactions on Sustainable Energy, 2, 50-59.
[10]	Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C. and Liu, H. (1998) The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis. Proceedings of the Royal Society London A, 454, 903-995. http://dx.doi.org/10.1098/rspa.1998.0193
[11]	Ye, L. and Liu, P. (2011) Combined Model Based on EMD-SVM for Short-Term Wind Power Prediction. Proceedings of the CSEE, 31, 102-108.
[12]	Sun, C., Yuan, Y. andLi, Q. (2012) A New Method for Wind Speed Forecasting Based on Empirical Mode Decomposition and Improved Persistence Approach. Conference on Power & Energy (IPEC2012), Ho ChiMinh City, 659-664.
[13]	Hu, J., Wang, J. and Zeng, G. (2013) A Hybrid Forecasting Approach Applied to Wind Speed Time Series. Renewable Energy, Elsevier, 60, 185-194. http://dx.doi.org/10.1016/j.renene.2013.05.012
[14]	Lin, A.J., Shang, P.J., Feng, G.C. and Zhong, B. (2012) Application of Empirical Mode Decomposition Combined with K-Nearest Neighbors Approach in Financial Time Series Forecasting. Fluctuation and Noise Letters, 11. http://dx.doi.org/10.1142/S0219477512500186
[15]	Rilling, G., Flandrin, P. and Goncalves, P. (2003) On Empirical Mode Decomposition and Its Algorithms. IEEE- EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP2003), 3, 8-11.
[16]	Solomatine, D.P., Maskey, M. andShrestha, D.L. (2008) Instance-Based Learning Compared to Other Data-Driven Methods in Hydrological Forecasting. Hydrological Processes, 22, 275-287. http://dx.doi.org/10.1002/hyp.6592
[17]	Lall, U. and Sharma, A. (1996) A Nearest Neighbor Bootstrap for Resampling Hydrologic Time Series. Water Resources Research, 32, 679-693. http://dx.doi.org/10.1029/95WR02966