A robust combination approach for short-term wind speed forecasting and analysis - Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

Energy

Volumn 93, Issue , 2015, Pages 41-56

  Wang, Jianzhou ^b   Hu, Jianming ^a  

^a LANZHOU UNIVERSITY   (China)

^b DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Empirical Wavelet Transform; Extreme Learning Machine; Gaussian Process Regression; Support Vector Machine; Wind speed forecasting

Indexed keywords

ENGINES; FILTER BANKS; FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; REGRESSION ANALYSIS; SPEED; STOCHASTIC SYSTEMS; SUPPORT VECTOR MACHINES; WAVELET TRANSFORMS; WIND EFFECTS; WIND POWER;

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; ELM (EXTREME LEARNING MACHINE); EXTREME LEARNING MACHINE; GAUSSIAN PROCESS REGRESSION; PROBABILISTIC INFORMATION; SHORT-TERM WIND SPEED FORECASTING; SVM(SUPPORT VECTOR MACHINE); WIND SPEED FORECASTING;

WIND;

EMPIRICAL ANALYSIS; FORECASTING METHOD; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; TRANSFORM; WAVELET ANALYSIS; WIND FARM; WIND VELOCITY;

CHINA;

EID: 84949681323     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2015.08.045     Document Type: Article

References (48)

1. Tascikaraoglu A., Uzunoglu M. A review of combined approaches for prediction of short-term wind speed and power. Renew Sustain Energy Rev 2014, 34:243-254.
2. Nor Khalid Mohamed, Shaaban Mohamed, Rahman Hasimah Abdul Feasibility assessment of wind energy resources in Malaysia based on NWP models. Renew Energy 2014, 62:147-154.
3. Cassola Federico, Burlando Massimiliano Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Appl Energy 2012, 99:154-166.
4. Erdem Ergin, Shi Jing ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 2011, 88:1405-1414.
5. Liu Hui, Tian Hong-qi, Li Yan-fei An EMD-recursive ARIMA method to predict wind speed. J Wind Eng Ind Aerodyn 2015, 141:27-38.
6. Kavasseri Rajesh G., Seetharaman Krithika Day-ahead wind speed forecasting using f-ARIMA models. Renew Energy 2009, 34:1388-1393.
7. Yang Dazhi, Sharma Vishal, Ye Zhen, Lim Lihong Idris, Zhao Lu, Aryaputera Aloysius W. Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy 2015, 81:111-119.
8. Li Yunhua, Ling Lina, Chen Jiantao Combined grey prediction fuzzy control law with application to road tunnel ventilation system. J Appl Res Technol 2015, 13:313-320.
9. Wang Jujie, Zhang Wenyu, Li Yaning, Wang Jianzhou, Dang Zhangli Forecasting wind speed using empirical mode decomposition and Elman neural network. Appl Soft Comput 2014, 23:452-459.
10. Ren Chao, An Ning, Wang Jianzhou, Li Lian, Hu Bin, Shang Duo Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowledge-Based Syst 2014, 56:226-239.
11. Shi Jing, Guo Jinmei, Zheng Songtao Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew Sustain Energy Rev 2012, 16:3471-3480.
12. Cao Qing, Ewing Bradley T., Thompson Mark A. Forecasting wind speed with recurrent neural networks. Eur J Oper Res 2012, 221:148-154.
13. Sideratos George, Hatziargyriou Nikos D. Probabilistic wind power forecasting using radial basis function neural networks. IEEE Trans Power Syst 2012, 27:1788-1796.
14. López Paz, Maseda Francisco Wind speed estimation using multilayer perceptron Ramón Velo. Energy Convers Manage 2014, 81:1-9.
15. Zhu Bo, Chen Minyou, Wade Neal, Li Ran A prediction model for wind farm power generation based on fuzzy modelling. Int Conf Environ Sci Eng 2012, 12:122-129.
16. Yin Xiu-xing, Lin Yong-gang, Li Wei, Gu Ya-jing, Liu Hong-wei, Lei Peng-fei A novel fuzzy integral sliding mode current control strategy for maximizing wind power extraction and eliminating voltage harmonics. Energy 2015, 85:677-686.
17. Liu Da, Niu Dongxiao, Wang Hui, Fan Leilei Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 2014, 62:592-597.
18. Kong Xiaobing, Liu Xiangjie, Shi Ruifeng, Lee Kwang Y. Wind speed prediction using reduced support vector machines with feature selection. Neurocomputing 2 December 2015, 169:449-456.
19. Hu Jianming, Wang Jianzhou, Zeng Guowei A hybrid forecasting approach applied to wind speed time series. Renew Energy 2013, 60:185-194.
20. Osório G.J., Matias J.C.O., Catalão J.P.S. Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew Energy 2015, 75:301-307.
21. Kanna Bhaskar S., Singh N. AWNN-assisted wind power forecasting using feedforward neural network. IEEE Trans Sustain Energy 2012, 3:15.
22. Tascikaraoglu A., Uzunoglu M., Vural Tascikaraoglu B. The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems. Appl Energy 2012, 94:156-165.
23. Huang Norden E., Shen Zheng, Long Steven R., Wu Manli C., Shih Hsing H., Zheng Quanan, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc A Math Phys Eng Sci 1998, 454:903-995.
24. Guo Zhenhai, Zhao Weigang, Lu Haiyan, Wang Jianzhou Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew Energy 2012, 37:241-249.
25. Liu Hui, Chen Chao, Tian Hong-qi, Li Yan-fei A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renew Energy 2012, 48:545-556.
26. Liu Hui, Tian Hong-qi, Li Yan-fei Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions. Energy Convers Manage 2015, 89:1-11.
27. Flores Juan J., Graff Mario, Rodriguez Hector Evolutive design of ARMA and ANN models for time series forecasting. Renew Energy 2012, 44:225-230.
28. Li Ran, Ke Yongqin, Zhang Xiaoqian Forecasting of wind speed with least squares support vector machine based on genetic algorithm. Proceedings of the International conference on consumer electronics, communications and networks (CECNet) 2011, 358-361.
29. Amjady Nima, Keynia Farshid, Zareipour Hamidreza Short-term wind power forecasting using ridgelet neural network. Electr Power Syst Res 2011, 81:2099-2107.
30. Pandit Manjaree, Chaudhary Vishal, Dubey Hari Mohan, Panigrahi B.K. Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection. Electr Power Energy Syst 2015, 73:259-272.
31. Hervás Martínez C., Gutiérrez P.A., Fernández1 J.C., Salcedo-Sanz S., Portilla-Figueras A., Pérez-Bellido A., et al. Hyperbolic tangent basis function neural networks training by hybrid evolutionary programming for accurate short term wind speed prediction. Proceedings of the Ninth international conference on intelligent systems design and applications (ISDA'09) 2009, 193-198.
32. Han Shuang, Li Jinshan, Liu Yongqian Tabu search algorithm optimized ANN model for wind power prediction with NWP. Energy Proc 2011, 12:733-740.
33. Jabbari ghadi M., Hakimi gilani S., sharifiyan A., Afrakhteh H. A new method for short-term wind power forecasting. Proceedings of 17th conference on electrical power distribution networks (EPDC) 2012, 1-6.
34. Sánchez Ismael Adaptive combination of forecasts with application to wind energy. Int J Forecast 2008, 24:679-693.
35. Han Shuang, Liu Yongqian The study of wind power combination prediction. Proceedings of the Asia-Pacific power and energy engineering conference (APPEEC) 2010, 1-4.
36. Ali Ehab S. Speed control of induction motor supplied by wind turbine via imperialist competitive algorithm. Energy 2015, 1-8.
37. Jiang Yu, Song Zhe, Kusiak Andrew Very short-term wind speed forecasting with Bayesian structural break model. Renew Energy 2013, 50:637-647.
38. Bessa Ricardo J., Miranda V., Botterud A., Zhou Z., Wang J. Time-adaptive quantile-copula for wind power probabilistic forecasting. Renew Energy 2012, 40:29-39.
39. Pinson Pierre Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions. J R Stat Soc Ser C Appl Stat 2012, 61:555-576.
40. Pinson Pierre, Kariniotakis George Conditional prediction intervals of wind power generation. IEEE Trans Power Syst 2010, 25:1845-1856.
41. Kou Peng, Gao Feng, Guan Xiaohong Sparse online warped Gaussian process for wind power probabilistic forecasting. Appl Energy 2013, 108:410-428.
42. Baran Sándor Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components. Comput Stat Data Anal 2014, 75:227-238.
43. Gilles Jérôme Empirical wavelet transform. IEEE Trans signal Process 2013, 61:3999-4010.
44. Tsay Ruey S. Analysis of financial time series 2010, John Wiley & Sons, Inc. 3rd ed.
45. Wang Haifeng, Hu Dejin Comparison of SVM and LS-SVM for regression. Neural Networks and Brain, 2005. ICNN&B'05. International Conference on, IEEE 2005, vol. 1:279-283.
46. Huang GuangBin, Zhou Hongming, Ding Xiaojian, Zhang Rui Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern 2012, 42:513-528.
47. Vanhatalo Jarno, Riihimäki Jaakko, Hartikainen Jouni, Jylänki Pasi, Tolvanen Ville, Vehtari Aki GPstuff: Bayesian modeling with Gaussian processes. J Mach Learn Res 2013, 14:1175-1179.
48. Li Gong, Shi Jing, Zhou Junyi Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renew Energy 2011, 36:352-359.