Abstract
Short-term wind speed prediction is beneficial to guarantee the safety of wind power utilization and reduce the cost of wind power generation. As a kind of the powerful artificial intelligent algorithms, support vector regression (SVR) has been successfully employed in solving forecasting problems. However, due to the intrinsic complexity and multi-patterns of wind speed fluctuations, it is regarded as one of the most challenging applications for wind speed prediction. To alleviate the influence of complexity and capture these different patterns, this study proposes a novel approach named SIE–WDA–GA–SVR for short-term wind speed prediction, which applies the seasonal information extraction (SIE) and wavelet decomposition algorithm (WDA) into hybrid model that integrates the genetic algorithm (GA) into SVR. First, the proposed approach uses SIE to decompose the original wind speed into seasonal and trend components, and the seasonal indices are calculated by SIE. Second, the proposed approach uses WDA to decompose the trend component into both the approximate and the detailed scales. Third, the proposed approach uses GA–SVR to forecast the approximated and detailed scales, respectively. Then, the prediction values of the trend component can be obtained by integrating the prediction values of the approximated scale into the prediction values of the detailed scale. By integrating the seasonal indices into the prediction values of trend component, we can obtain the final forecasting results of the original wind speed. Moreover, the partial autocorrelation function is used to determine the number of input dimension for the SVR, and the GA is used to select the parameters of the SVR. Four real wind speed datasets are used as test samples to verify the proposed approach. Experimental results indicate that the proposed approach outperforms other benchmark models in four statistical error measures, and can improve the forecasting accuracy of wind speed.
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References
Ma L, Luan SY, Jiang CW, Liu HL, Zhang Y (2009) A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev 13:915–20
Bigdeli N, Afshar K, Gazafroudi AS, Ramandi MY (2013) A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada. Renew Sustain Energy Rev 27:20–9
Abdelkafi A, Masmoudi A, Krichen L (2013) Experimental investigation on the performance of an autonomous wind energy conversion system. Int J Electr Power Energy Syst 44(1):581–90
Liu H, Tian HQ, Chen C, Li YF (2013) An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization. Electr Power Energy Syst 52:161–73
Georgilakis PS (2008) Technical challenges associated with the integration of wind power into power systems. Renew Sustain Energy Rev 12(3):852–63
Foley AM, Leahy PG, Marvuglia A, McKeogh EJ (2012) Current methods and advances in forecasting of wind power generation. Renew Energy 37(1):1–8
Wang JJ, Zhang WY, Wang JZ, Han TT, Kong LB (2014) A novel hybrid approach for wind speed prediction. Inform Sci 273:304–18
Kavasseri RG, Seetharaman K (2009) Day-ahead wind speed forecasting using f-ARIMA models. Renew Energy 34:1388–93
Mohandes M, Halawani T, Rehman S, Hussain AA (2004) Support vector machines for wind speed prediction. Renew Energy 29:939–47
Morales JM, Mínguez R, Conejo AJ (2010) A methodology to generate statistically dependent wind speed scenarios. Appl Energy 87(3):843–55
Fadare DA (2010) The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria. Appl Energy 87(3):934–42
Wang JJ, Zhang WY, Li YN, Wang JZ, Dang ZL (2014) Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl Soft Comput 23:452–9
Lalarukh K, Yasmin ZJ (1997) Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan. Sol Energy 61(1):23–32
Louka P, Galanis G, Siebert N, Kariniotakis G, Katsafados P, Pytharoulis I, Kallos G (2008) Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. J Wind Eng Ind Aerod 96:2348–62
Malmberg A, Holst U, Holst J (2005) Forecasting near-surface ocean winds with Kalman filter techniques. Ocean Eng 32:273–91
Bivona S, Bonanno G, Burlon R, Gurrera D, Leone C (2011) Stochastic models for wind speed forecasting. Energy Convers Manage 52:1157–65
Shamshad A, Bawadi MA, Hussin WMAW, Majid TA, Sanusi SAM (2005) First and second order Markov chain models for synthetic generation of wind speed time series. Energy 30:693–708
Cadenas E, Rivera W (2009) Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks. Renew Energy 34:274–8
Li G, Shi J (2010) On comparing three artificial neural networks for wind speed forecasting. Appl Energy 87:2313–20
Flores P, Tapia A, Tapia G (2005) Application of a control algorithm for wind speed prediction and active power generation. Renew Energy 30(4):523–36
Zhou JY, Shi J, Li G (2011) Fine tuning support vector machines for short-term wind speed forecasting. Energy Convers Manage 52:1990–8
Hu QH, Zhang SG, Xie ZX, Mi JS, Wan J (2014) Noise model based \(\upnu \)-support vector regression with its application to short-term wind speed forecasting. Neural Netw 57:1–11
Kavousi-Fard A, Khosravi A, Nahavandi S (2016) A new fuzzy-based combined prediction interval for wind power forecasting. IEEE Trans Power Syst 31(1):18–26
Santamaría-Bonfil G, Reyes-Ballesteros A, Gershenson C (2016) Wind speed forecasting for wind farms: a method based on support vector regression. Renew Energy 85:790–809
Noorollahi Y, Jokar MA, Kalhor A (2016) Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. Energy Convers Manage 115:17–25
Dowell J, Pinson P (2016) Very short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE T Smart Grid 7(2):763–70
Ghorbani MA, Khatibi R, FazeliFard MH, Naghipour L, Makarynskyy O (2016) Short-term wind speed predictions with machine learning techniques. Meteorol Atmos Phys 128:57–72
Men ZX, Yee E, Lien FS, Wen DY, Chen YS (2016) Short-term wind speed and power forecasting using an ensemble of mixture density neural networks. Renew Energy 87:203–11
Heinermann J, Kramer O (2016) Machine learning ensembles for wind power prediction. Renew Energy 89:671–9
Sun W, Liu MH (2016) Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China. Energy Convers Manage 114:197–208
Doucoure B, Agbossou K, Cardenas A (2016) Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data. Renew Energy 92:202–11
Wang SX, Zhang N, Wu L, Wang YM (2016) Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew Energy 94:629–36
Meng AB, Ge JF, Yin H, Chen SZ (2016) Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm. Energy Convers Manage 114:75–88
Hu WB, Yan LP, Liu KZ, Wang H (2016) A Short-term traffic flow forecasting method based on the hybrid PSO-SVR. Neural Process Lett 43:155–72
Cao Q, Parry ME (2009) Neural network earnings per share forecasting models: a comparison of backward propagation and the genetic algorithm. Decis Support Syst 47:32–41
Gu JR, Zhu MC, Jiang LGY (2011) Housing price forecasting based on genetic algorithm and support vector machine. Expert Syst Appl 38:3383–6
Zhang GP, Qi M (2005) Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160:501–14
Wang JJ (2014) A hybrid wavelet transform based short-term wind speed forecasting approach. Sci World J 914127:1–12
Cohen A, Daubechies I, Vial P (1993) Wavelets on the interval and fast wavelet transform. Appl Comput Harmon A 1(1):54–81
Gencay R, Selcuk F, Whitcher B (2001) Differentiating intraday seasonalities through wavelet multi-scaling. Phys A 289(3–4):543–56
Cannas B, Fanni A, See L, Sias G (2006) Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Phys Chem Earth 31(18):1164–71
Guo ZH, Wu J, Lu HY, Wang JZ (2011) A case study on a hybrid wind speed forecasting method using BP neural network. Knowl-Based Syst 24:1048–56
Donoho DL, Johnstone IM (1998) Minimax estimation via wavelet shrinkage. Ann Stat 26:879–921
Chang SG, Yu B, Vetterli M (2000) Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Trans Image Process 9:1522–31
Ramsey JB (2002) Wavelets in economics and finance: past and future. Stud Nonlinear Dyn Econom 6:1–27
Li T, Li Q, Zhu S (2003) A survey on wavelet applications in data mining. Sigkdd Explor 4:49–68
Hussain MS, Reaz MBI, Mohd-Yasin F, Ibrahimy MI (2009) Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction. Expert Syst 26:35–48
Lu CJ (2010) Integrating independent component analysis-based de-noising scheme with neural network for stock price prediction. Expert Syst Appl 37:7056–64
Goswami JC, Chan AK (1999) Fundamentals of wavelets: theory, algorithms, and applications. Wiley, Hoboken, pp 149–52
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999
Wang H, Zhao W (2009) ARIMA model estimated by particle Swarm optimization algorithm for consumer price index forecasting, lecture notes in computer science. Artif Intell Comput Intell 5855:48–58
Guo ZH, Zhao WG, Lu HY, Wang JZ (2012) Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew Energy 37:241–9
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant No. 71501101), the Natural Science Foundation of Jiangsu Province (Grant No. BK20150928), the Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (Grant No. 2015SJB063), the Qing Lan Project, the National Natural Science Foundation of China (Grant No. 1170011208, 91546117, and 61502242), the National Social Science Fund of China (Grant No. 16ZDA047), the Startup Foundation for Introducing Talent of NUIST (S8113097001), the Project Funded by the Flagship Major Development of Jiangsu Higher Education Institutions, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Top-notch Academic Programs Project of Jiangsu Higher Education Institutions.
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Wang, J., Li, Y. Short-Term Wind Speed Prediction Using Signal Preprocessing Technique and Evolutionary Support Vector Regression. Neural Process Lett 48, 1043–1061 (2018). https://doi.org/10.1007/s11063-017-9766-4
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DOI: https://doi.org/10.1007/s11063-017-9766-4