DOI QR코드

DOI QR Code

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)
  • 투고 : 2018.05.26
  • 심사 : 2019.08.06
  • 발행 : 2019.12.25

초록

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.

키워드

과제정보

연구 과제 주관 기관 : National Science Foundation of China, Zhejiang Provincial Natural Science Foundation of China, Central Universities of China

참고문헌

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. Cigizoglu, H.K. (2005), "Generalized regression neural network in monthly flow forecasting", Civ. Eng. Environ. Syst., 22(2), 71-81. DOI: 10.1080/10286600500126256.
  7. 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.
  8. 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.
  9. Engelbrecht, A.P. (2006), "Fundamentals of computational swarm intelligence", Wiley, Hoboken, NJ, USA.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. Holland, J.H. (1973), "Genetic algorithms and the optimal allocation of trials", Siamj. Comput., 2(2), 88-105. DOI:10.1137/0202009.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", P. IEEE. Int. Conf. Neur. Net., 4, 1942-1948. DOI: 10.1109/ICNN.1995.488968.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. Lazarevska, E. (2016), "Wind speed prediction with extreme learning machine", IEEE. Int. Conf. Intell. Syst., 154-159. DOI:10.1109/IS.2016.7737415.
  32. 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.
  33. Maulik, U. and Bandyopadhyay, S. (2000), "Genetic algorithmbased clustering technique", Pattern Recognit., 33(9), 1455-1465. DOI: 10.1016/S0031-3203(99)00137-5.
  34. McLachlan, G.J. and Peel, D. (2000), "Finite mixture models", Wiley, New York, N.Y., USA.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. Specht, D.F. (1991), "A general regression neural network", IEEE. T. Neur. Net., 2(6), 568-576. DOI: 10.1109/72.97934.
  41. Specht, D.F. (1992), "Enhancements to probabilistic neural networks", Neur. Net., 761-768. DOI:10.1109/IJCNN.1992.287095.
  42. Specht, D.F. (1993), "The general regression neural networkrediscovered", Neur. Net., 6(7), 1033-1034. DOI:10.1016/S0893-6080(09)80013-0.
  43. Werbos, P.J. (1974), "Beyond regression: new tools for prediction and analysis in the behavioral sciences" Harvard University, Cambridge, MA.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.
  55. 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.
  56. 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.
  57. 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.