Support vector regression based on grid-search method for short-term wind power forecasting. (English) Zbl 1442.62726

Summary: The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.


62P12 Applications of statistics to environmental and related topics
62M20 Inference from stochastic processes and prediction


Full Text: DOI


[1] Mohandes, M. A.; Halawani, T. O.; Rehman, S.; Hussain, A. A., Support vector machines for wind speed prediction, Renewable Energy, 29, 6, 939-947 (2004)
[2] Kramer, O.; Gieseke, F., Short-term wind energy forecasting using support vector regression, Advances in Intelligent and Soft Computing, 87, 271-280 (2011)
[3] Milligan, M.; Porter, K.; DeMeo, E.; Denholm, P.; Holttinen, H.; Kirby, B.; Miller, N.; Mills, A.; O’Malley, M.; Schuerger, M.; Soder, L., Wind power myths debunked, IEEE Power and Energy Magazine, 7, 6, 89-99 (2009)
[4] Alessandrini, S.; Sperati, S.; Pinson, P., A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data, Applied Energy, 107, 271-280 (2013)
[5] Famili, F.; Shen, W. M.; Weber, R.; Simoudis, E., Data pre-processing and intelligent data analysis, Intelligent Data Analysis, 1, 1-4, 3-23 (1997)
[6] Costa, A.; Crespo, A.; Navarro, J.; Lizcano, G.; Madsen, H.; Feitosa, E., A review on the young history of the wind power short-term prediction, Renewable and Sustainable Energy Reviews, 12, 6, 1725-1744 (2008)
[7] Osowski, S.; Garanty, K., Forecasting of the daily meteorological pollution using wavelets and support vector machine, Engineering Applications of Artificial Intelligence, 20, 6, 745-755 (2007)
[8] Chen, B.-J.; Chang, M.-W.; Lin, C.-J., Load forecasting using support vector machines: a study on EUNITE competition 2001, IEEE Transactions on Power Systems, 19, 4, 1821-1830 (2004)
[9] Chang, W.-Y., An RBF neural network combined with OLS algorithm and genetic algorithm for short-term wind power forecasting, Journal of Applied Mathematics, 2013 (2013) · Zbl 1266.62095
[10] Che, Y. B.; Zhang, W.; Ge, L. J.; Zhang, J. J., A two-stage wind grid inverter with boost converter, Journal of Applied Mathematics, 2014 (2014)
[11] Song, Y. D.; Cao, Q.; Du, X. Q.; Karimi, H. R., Control strategy based on wavelet transform and neural network for hybrid power system, Journal of Applied Mathematics, 2013 (2013)
[12] Lei, M.; Shiyan, L.; Chuanwen, J.; Hongling, L.; Yan, Z., A review on the forecasting of wind speed and generated power, Renewable and Sustainable Energy Reviews, 13, 4, 915-920 (2009)
[13] Li, T.-F.; Jia, W.; Zhou, W.; Ge, J.-K.; Liu, Y.-C.; Yao, L.-Z., Incomplete phase space reconstruction method based on subspace adaptive evolution approximation, Journal of Applied Mathematics, 2013 (2013) · Zbl 1397.62312
[14] Do Hoai, N.; Udo, K.; Mano, A., Downscaling global weather forecast outputs using ANN for flood prediction, Journal of Applied Mathematics, 2011 (2011) · Zbl 1213.86001
[15] Sánchez, I., Short-term prediction of wind energy production, International Journal of Forecasting, 22, 1, 43-56 (2006)
[16] Foley, A. M.; Leahy, P. G.; Marvuglia, A.; McKeogh, E. J., Current methods and advances in forecasting of wind power generation, Renewable Energy, 37, 1, 1-8 (2012)
[17] Botterud, A.; Wang, J.; Miranda, V.; Bessa, R. J., Wind power forecasting in U.S. electricity markets, Electricity Journal, 23, 3, 71-82 (2010)
[18] Stathopoulos, C.; Kaperoni, A.; Galanis, G.; Kallos, G., Wind power prediction based on numerical and statistical models, Journal of Wind Engineering and Industrial Aerodynamics, 112, 25-38 (2013)
[19] Famili, F.; Ouyang, J., Data mining: understanding data and disease modeling, Proceedings of the 21st IASTED International Multi-Conference on Applied Informatics
[20] Noriega, G.; Pasupathy, S., Application of Kalman filtering to real-time preprocessing of geophysical data, IEEE Transactions on Geoscience and Remote Sensing, 30, 5, 897-910 (1992)
[21] Allen, D. P., A frequency domain Hampel filter for blind rejection of sinusoidal interference from electromyograms, Journal of Neuroscience Methods, 177, 2, 303-310 (2009)
[22] Davies, L.; Gather, U., The identification of multiple outliers, Journal of the American Statistical Association, 88, 423, 782-792 (1993) · Zbl 0797.62025
[23] Pearson, R. K., Outliers in process modeling and identification, IEEE Transactions on Control Systems Technology, 10, 1, 55-63 (2002)
[24] Mallat, S., A Wavelet Tour of Signal Processing: The Sparse Way (2009), Academic Press · Zbl 1170.94003
[25] Shalom, R., Mixed Representation and Their Applications, Lecture Notes (2011), Technion-Israel Institute of Technology
[26] Steinbuch, M.; Molengraft, M. J. G., Wavelet theory and applications: a literature study (2005), Eindhoven University of Technology
[27] Donoho, D. L., De-noising by soft-thresholding, IEEE Transactions on Information Theory, 41, 3, 613-627 (1995) · Zbl 0820.62002
[28] Mallat, S. G., A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, 674-693 (1989) · Zbl 0709.94650
[29] Vaidyanathan, P. P., Quadrature mirror filter banks, M-band extensions and perfect-reconstruction techniques, IEEE ASSP Magazine, 4, 3, 4-20 (1987)
[30] Mallat, S. G., A theory for multiresolution signal decomposition: the wavelet representation, 668 (1987), CIS
[31] Buckheit, J.; Chen, S.; Donoho, D.; Johnstone, I.; Scargle, J. D., Wavelab Reference Manual, Version 0. 700 (1995)
[32] Dallal, G. E., Partial Correlation Coefficients
[33] Drucker, H.; Burges, C. J.; Kaufman, L.; Smola, A.; Vapnik, V., Support vector regression machines, Advances in Neural Information Processing Systems, 9, 155-161 (1997)
[34] Suykens, J. A. K.; Vandewalle, J., Least squares support vector machine classifiers, Neural Processing Letters, 9, 3, 293-300 (1999)
[35] Burges, C. J. C., A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 2, 121-167 (1998)
[36] Cristianini, N.; Shawe-Taylor, J., An Introduction To Support Vector Machines and Other Kernel-Based Learning Methods (2000), Cambridge University Press
[37] Shi, F.; Wang, X.; Yu, L.; Li, Y., 30 Cases MATLAB Neural Network Analysis (2010), Beijing, China: Beijing University of Aeronautics and Astronautics Press, Beijing, China
[38] Chang, C. C.; Lin, C. J., LIBSVM: a library for support vector machines
[40] Bengio, Y.; Grandvalet, Y., No unbiased estimator of the variance of K-fold cross-validation, Journal of Machine Learning Research, 5, 1089-1105 (2004) · Zbl 1222.68145
[41] Arlot, S.; Celisse, A., A survey of cross-validation procedures for model selection, Statistics Surveys, 4, 40-79 (2010) · Zbl 1190.62080
[42] Rodriguez, J. D.; Perez, A.; Lozano, J. A., Sensitivity analysis of k-fold cross validation in prediction error estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 3, 569-575 (2010)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.