Browse > Article
http://dx.doi.org/10.12989/sss.2019.24.6.733

Machine learning approaches for wind speed forecasting using long-term monitoring data: a comparative study  

Ye, X.W. (Department of Civil Engineering, Zhejiang University)
Ding, Y. (Department of Civil Engineering, Zhejiang University)
Wan, H.P. (Department of Civil Engineering, Zhejiang University)
Publication Information
Smart Structures and Systems / v.24, no.6, 2019 , pp. 733-744 More about this Journal
Abstract
Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.
Keywords
structural health monitoring; wind speed prediction; machine learning; optimization algorithm; finite mixture method;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Xu, R.L., Xu, X., Zhu, B. and Chen, M. (2011), "The application of genetic-neural network on wind power prediction" Int. Conf. Inf. Comput. Appl., 379-386. DOI: 10.1007/978-3-642-27452-7_52.
2 Yang, D. and Han, F. (2014), "An improved ensemble of extreme learning machine based on attractive and repulsive particle swarm optimization" Int. Conf. Intell. Comput., 213-220. DOI:10.1007/978-3-319-09333-8_23.
3 Ye, X.W., Ni, Y.Q., Wong, K.Y. and Ko, J.M. (2012), "Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data", Eng. Struct., 45, 166-176. DOI: 10.1016/j.engstruct.2012.06.016.   DOI
4 Ye, X.W., Ni, Y.Q., Wai, T.T., Wong, K.Y., Zhang, X.M. and Xu, F. (2013), "A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification", Smart. Struct. Syst., 12(3), 363-379. https://doi.org/10.12989/sss.2013.12.3_4.363.   DOI
5 Ye, X.W., Yi, T.H., Wen, C. and Su, Y.H. (2015a), "Reliabilitybased assessment of steel bridge deck using a mesh-insensitive structural stress method", Smart. Struct. Syst., 16(2), 367-382. https://doi.org/10.12989/sss.2015.16.2.367.   DOI
6 Ye, X.W., Yi, T.H., Dong, C.Z., Liu, T. and Bai, H. (2015b), "Multi-point displacement monitoring of bridges using a visionbased approach", Wind Struct., 20(2), 315-326. https://doi.org/10.12989/was.2015.20.2.315.   DOI
7 Ye, X.W., Dong, C.Z. and Liu, T. (2016a), "Force monitoring of steel cables using vision-based sensing technology:methodology and experimental verification", Smart. Struct. Syst., 18(3), 585-599. https://doi.org/10.12989/sss.2016.18.3.585.   DOI
8 Ping, J., Zeng, Z., Chen, J. and Huang, T. (2014), "Generalized regression neural networks with k-fold cross-validation for displacement of landslide forecasting", Adv. Neur. Net., 533-541. DOI: 10.1007/978-3-319-12436-0_59.
9 Refaeilzadeh, P., Lei, T. and Liu, H. (2016), "Cross-validation", Encyclopedia of Database Sys., 532-538. DOI: 10.1007/978-0-387-39940-9_565.
10 Shao, Z., Meng, J.E. and Ning, W. (2016), "An efficient leaveone-out cross-validation-based extreme learning machine (elooelm) with minimal user intervention", IEEE T. Cybern., 46(8), 1939-1951. DOI: 10.1109/TCYB.2015.2458177.   DOI
11 Specht, D.F. (1991), "A general regression neural network", IEEE. T. Neur. Net., 2(6), 568-576. DOI: 10.1109/72.97934.   DOI
12 Specht, D.F. (1992), "Enhancements to probabilistic neural networks", Neur. Net., 761-768. DOI:10.1109/IJCNN.1992.287095.
13 Specht, D.F. (1993), "The general regression neural networkrediscovered", Neur. Net., 6(7), 1033-1034. DOI:10.1016/S0893-6080(09)80013-0.   DOI
14 Werbos, P.J. (1974), "Beyond regression: new tools for prediction and analysis in the behavioral sciences" Harvard University, Cambridge, MA.
15 Wen, X.L., Huang, J.C., Sheng, D.H. and Wang, F.L. (2010), "Conicity and cylindricity error evaluation using particle swarm optimization" Precis. Eng., 34(2), 338-344. DOI:10.1016/j.precisioneng.2009.08.002.   DOI
16 Ye, X.W., Su, Y.H., Xi, P.S., Chen, B. and Han, J.P. (2016d), "Statistical analysis and probabilistic modeling of WIM monitoring data of an instrumented arch bridge", Smart. Struct. Syst., 17(6), 1087-1105. https://doi.org/10.12989/sss.2016.17.6.1087.   DOI
17 Ye, X.W., Yi, T.H., Dong, C.Z. and Liu, T. (2016b), "Visionbased structural displacement measurement: system performance evaluation and influence factor analysis", Measurement, 88, 372-384. DOI:10.1016/j.measurement.2016.01.024.   DOI
18 Wang, S., Zhang, N., Wu, L. and Wang, Y. (2016), "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and ga-bp neural network method." Renew. Energ., 94, 629-636. DOI: 10.1016/j.renene.2016.03.103.   DOI
19 Wang, Y. and Peng, H. (2018), "Underwater acoustic source localization using generalized regression neural network." J. Acoust. Soc. Am., 143(4), 2321-2331. DOI: 10.1121/1.5032311.   DOI
20 Ye, X.W., Dong, C.Z. and Liu, T. (2016c), "Image-based structural dynamic displacement measurement using different multi-object tracking algorithms", Smart. Struct. Syst., 17(6), 935-956. https://doi.org/10.12989/sss.2016.17.6.935.   DOI
21 Ye, X.W., Yi, T.H., Su, Y.H., Liu, T. and Chen, B. (2017), "Strain-based structural condition assessment of an instrumented arch bridge using FBG monitoring data", Smart. Struct. Syst., 20(2), 139-150. https://doi.org/10.12989/sss.2017.20.2.139.   DOI
22 Cigizoglu, H.K. (2005), "Generalized regression neural network in monthly flow forecasting", Civ. Eng. Environ. Syst., 22(2), 71-81. DOI: 10.1080/10286600500126256.   DOI
23 Assareh, E., Behrang, M.A., Assari, M.R. and Ghanbarzadeh, A. (2010), "Application of pso (particle swarm optimization) and ga (genetic algorithm) techniques on demand estimation of oil in iran", Energ., 35(12), 5223-5229. DOI:10.1016/j.energy.2010.07.043.   DOI
24 Beheshti, Z., and Shamsuddin, S.M.H. (2014), "Capso: centripetal accelerated particle swarm optimization", Inf. Sci., 258, 54-79. DOI: 10.1016/j.ins.2013.08.015.   DOI
25 Braganeto, U.M. and Dougherty, E.R. (2004), "Is cross-validation valid for small-sample microarray classification?", Bioinform., 20(3), 374-380. DOI: 10.1093/bioinformatics/btg419.   DOI
26 Chen, K. and Yu, J. (2014), "Short-term wind speed prediction using an unscented kalman filter based state-space support vector regression approach", Appl. Energ., 113, 690-705. DOI:10.1016/j.apenergy.2013.08.025.   DOI
27 Cheung, K.S., Langevin, A. and Delmaire, H. (1997), "Coupling genetic algorithm with a grid search method to solve mixed integer nonlinear programming problems", Comput. Math. Appli., 34(12), 13-23. DOI: 10.1016/s0898-1221(97)00229-0.   DOI
28 Diana, G. and Tommasi, C. (2002), "Cross-validation methods in principal component analysis: a comparison", Stat. Method. Appl., 11(1), 71-82. DOI: 10.1007/bf02511446.   DOI
29 Engelbrecht, A.P. (2006), "Fundamentals of computational swarm intelligence", Wiley, Hoboken, NJ, USA.
30 Ding, S., Su, C. and Yu, J. (2011), "An optimizing bp neural network algorithm based on genetic algorithm", Artif. Intell. Rev., 36(2), 153-162. DOI: 10.1007/s10462-011-9208-z.   DOI
31 Guo, Z., Wu, J., Lu, H. and Wang, J. (2011), "A case study on a hybrid wind speed forecasting method using BP neural network", Knowledge-Based Syst., 24(7), 1048-1056. DOI:10.1016/j.knosys.2011.04.019.   DOI
32 Holland, J.H. (1973), "Genetic algorithms and the optimal allocation of trials", Siamj. Comput., 2(2), 88-105. DOI:10.1137/0202009.   DOI
33 Han, F., Yao, H.F. and Ling, Q.H. (2013), "An improved evolutionary extreme learning machine based on particle swarm optimization", Neurocomputing, 116, 87-93. DOI:10.1016/j.neucom.2011.12.062.   DOI
34 Heimes, F. and van Heuveln, B. (1998), "The normalized radial basis function neural network", P. IEEE. Int. Conf., 2, 1609-1614. DOI: 10.1109/ICSMC.1998.728118.
35 Hocaoglu, F.O. and Kurban, M. (2007), "The effect of missing wind speed data on wind power estimation", Int. Conf. Intell. Data Eng. Autom. Lea., 107-114. DOI: 10.1007/978-3-540-77226-2_12.
36 Huang, D.M., He, S.Q., He, X.H. and Zhu, X. (2017), "Prediction of wind loads on high-rise building using a bp neural network combined with pod", J. Wind Eng. Ind. Aerod., 170, 1-17. DOI:10.1016/j.jweia.2017.07.021.   DOI
37 Huang, G.B., Zhu, Q.Y. and Siew, C.K. (2006), "Extreme learning machine: theory and applications", Neurocomputing, 70(1-3), 489-501. DOI: 10.1016/j.neucom.2005.12.126.   DOI
38 Huang, G.B. and Lei, C. (2007), "Convex incremental extreme learning machine", Neurocomputing, 70(16), 3056-3062. DOI:10.1016/j.neucom.2007.02.009.   DOI
39 Jiang, P. and Chen, J. (2016), "Displacement prediction of landslide based on generalized regression neural networks with k-fold cross-validation", Neurocomputing, 198(7), 40-47. DOI:10.1016/j.neucom.2015.08.118.   DOI
40 Jadid, M.N. and Fairbairn, D.R. (1994), "The application of neural network techniques to structural analysis by implementing an adaptive finite-element mesh generation", Artif. Intel. Eng. Des. Anal. Manuf., 8(3), 177-191. DOI:10.1017/S0890060400001979.   DOI
41 Kai, C., Qi, L., Yao, L. and Yong, D. (2016), "Robust regularized extreme learning machine for regression using iteratively reweighted least squares", Neurocomputing., 230, 345-358. DOI:10.1016/j.neucom.2016.12.029.
42 Kassa, Y., Zhang, J., Zheng, D. and Wei, D. (2016), "A ga-bp hybrid algorithm based ann model for wind power prediction", IEEE. Smart Energ. Grid Eng., 158-163. DOI:10.1109/SEGE.2016.7589518.
43 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", P. IEEE. Int. Conf. Neur. Net., 4, 1942-1948. DOI: 10.1109/ICNN.1995.488968.
44 Khalid, M. and Savkin, A.V. (2012), "A method for short-term wind power prediction with multiple observation points", IEEE T. Power Syst., 27(2), 579-586. DOI:10.1109/tpwrs.2011.2160295.   DOI
45 Kumar, G. and Malik, H. (2016), "Generalized regression neural network based wind speed prediction model for western region of india", P. Comput. Sci., 93, 26-32. DOI:10.1016/j.procs.2016.07.177.   DOI
46 Landberg, L. (1999), "Short-term prediction of the power production from wind farms", J. Wind Eng. Ind. Aerod., 80(1-2), 207-220. DOI: 10.1016/S0167-6105(98)00192-5.   DOI
47 Huang, K., Dai, L. and Huang, S. (2010), "Wind prediction based on improved bp artificial neural network in wind farm", Int. Conf. El. Control Eng., 2548-2551. DOI:10.1109/iCECE.2010.630.
48 Li, H.Z., Guo, S., Li, C.J. and Sun, J.Q. (2013), "A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm", Knowledge-Based Syst., 37(2), 378-387. DOI:10.1016/j.knosys.2012.08.015.   DOI
49 Lei, M., Shiyan, L., Chuanwen, J., Hongling, L. and Yan, Z. (2009), "A review on the forecasting of wind speed and generated power", Renew. Sust. Energ. Rev., 13(4), 915-920. DOI: 10.1016/j.rser.2008.02.002.   DOI
50 Lee, C.Y. and He, Y.L. (2012), "Wind prediction based on general regression neural network", Int. Conf. Intell. Syst. Des. Eng. Appl., 617-620. DOI: 10.1109/ISdea.2012.520.
51 Lazarevska, E. (2016), "Wind speed prediction with extreme learning machine", IEEE. Int. Conf. Intell. Syst., 154-159. DOI:10.1109/IS.2016.7737415.
52 Liu, H., Mi, X. and Li, Y. (2018), "An experimental investigation of three new hybrid wind speed forecasting models using multidecomposing strategy and elm algorithm", Renew. Energ., 123,694-705. DOI: doi.org/10.1016/j.renene.2018.02.092.   DOI
53 Maulik, U. and Bandyopadhyay, S. (2000), "Genetic algorithmbased clustering technique", Pattern Recognit., 33(9), 1455-1465. DOI: 10.1016/S0031-3203(99)00137-5.   DOI
54 McLachlan, G.J. and Peel, D. (2000), "Finite mixture models", Wiley, New York, N.Y., USA.
55 Ni, Y.Q., Ye, X.W. and Ko, J.M. (2010), "Monitoring-based fatigue reliability assessment of steel bridges: analytical model and application", J. Eng. Mech., 136(12), 1563-1573. DOI:10.1061/(ASCE)ST.1943-541X.0000250.
56 Ni, Y.Q., Ye, X.W. and Ko, J.M. (2012), "Modeling of stress spectrum using long-term monitoring data and finite mixture distributions", J. Eng. Mech., 138(2), 175-183. DOI:10.1061/(ASCE)EM.1943-7889.0000313.   DOI
57 Li, X., Liu, Y. and Xin, W. (2009), "Wind speed prediction based on genetic neural network", IEEE. Conf. Ind. El. Appl., 2448-2451. DOI: 10.1109/ICIEA.2009.5138642.