Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM

References

• Baloch, M. H., J. Wang, and G. S. Kaloi. 2016. Stability and nonlinear controller analysis of wind energy conversion system with random wind speed. International Journal of Electrical Power & Energy Systems 79 (11):75–83. doi:10.1016/j.ijepes.2016.01.018.
   Google Scholar
• Cadenas, E., W. Rivera, R. Campos-Amezcua, and C. Heard. 2016. Wind speed prediction using a univariate ARIMA model and a multivariate NARX Model. Energies 9 (2):109. doi:10.3390/en9020109.
   Web of Science ®Google Scholar
• Chang, C. W., H. J. Lu, Y. R. Chang, and Y. D. Lee. 2017. An improved neural network-based approach for short-term wind speed and power forecast. Renewable Energy 105:301–11. doi:10.1016/j.renene.2016.12.071.
   Web of Science ®Google Scholar
• Ding, S., H. Zhao, Y. Zhang, X. Xu, and R. Nie. 2015. Extreme learning machine: Algorithm, theory and applications. Artificial Intelligence Review 44 (1):103–15. doi:10.1007/s10462-013-9405-z.
   Web of Science ®Google Scholar
• Dong, Q., Y. Sun, and P. Li. 2017. A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China. Renew Energy 102:241–57. doi:10.1016/j.renene.2016.10.030.
   Web of Science ®Google Scholar
• Du, J., Y. Liu, Y. Yu, and W. Yan. 2017a. A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms. Algorithms 10 (2):57. doi:10.3390/a10020057.
   Web of Science ®Google Scholar
• Du, P., J. Z. Wang, Z. H. Guo, and W. D. Wang. 2017b. Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting. Energy Conversion & Management 150:90–107. doi:10.1016/j.enconman.2017.07.065.
   Web of Science ®Google Scholar
• Du, P., J. Z. Wang, W. D. Yang, and T. Niu. 2018. Multi-step ahead forecasting in electrical power system using a hybrid forecasting system. Renewable Energy 122:533–50. doi:10.1016/j.renene.2018.01.113.
   Web of Science ®Google Scholar
• Erdem, E., and J. Shi. 2011. ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy 88 (4):1405–14. doi:10.1016/j.apenergy.2010.10.031.
   Web of Science ®Google Scholar
• Eshtay, M., H. Faris, and N. Obeid. 2018. Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems. Expert Systems with Applications 104:134–52. doi:10.1016/j.eswa.2018.03.024.
   Web of Science ®Google Scholar
• Feng, G. R., G. B. Huang, Q. P. Lin, and R. Gay. 2009. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks 20 (8):1352–57. doi:10.1109/TNN.2009.2024147.
   PubMed Web of Science ®Google Scholar
• Gani, A., K. Mohammadi, S. Shamshirband, T. A. Altameem, D. Petkovic, and S. Ch. 2016. A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm. Environmental Progress & Sustainable Energy 35 (3):867–75. doi:10.1002/ep.12262.
   Web of Science ®Google Scholar
• Hasanuzzaman, M., U. S. Zubir, N. I. Ilham, and S. Che. 2017. Global electricity demand, generation, grid system, and renewable energy polices: A review: Global electricity demand, generation, grid system, and renewable energy polices. Wiley Interdisciplinary Reviews Energy & Environment 6 (3):e222. doi:10.1002/wene.222.
   Web of Science ®Google Scholar
• Huang, G. B., and L. Chen. 2007. Convex incremental extreme learning machine. Neurocomputing 70:3056–62. doi:10.1016/j.neucom.2007.02.009.
   Web of Science ®Google Scholar
• Huang, G. B., and L. Chen. 2008. Enhanced random search based incremental extreme machine. Neurocomputing 71:3460–68. doi:10.1016/j.neucom.2007.10.008.
   Web of Science ®Google Scholar
• Huang, G. B., L. Chen, and C. K. Siew. 2006b. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Network 17 (4):879–92. doi:10.1109/TNN.2006.875977.
   PubMed Web of Science ®Google Scholar
• Huang, G. B., H. Zhou, X. Ding, and R. Zhang. 2012. Extreme learning machine for regression and multiclass classification. Systems, Man, and Cybernetics, Part B: IEEE Transactions on Cybernetics 42 (2):513–29. doi:10.1109/TSMCB.2011.2168604.
   PubMed Web of Science ®Google Scholar
• Huang, G. B., Q. Y. Zhu, and C. K. Siew. 2006a. Extreme learning machine: Theory and applications. Neurocomputing 70 (1–3):489–501. doi:10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Jiang, H. 2018. Model forecasting based on two-stage feature selection procedure using orthogonal greedy algorithm. Applied Soft Computing 63:110–23. doi:10.1016/j.asoc.2017.11.047.
   Web of Science ®Google Scholar
• Kadhem, A. A., N. I. A. Wahab, I. Aris, J. Jasni, and A. N. Abdalla. 2017. Advanced wind speed prediction model based on a combination of weibull distribution and an artificial neural network. Energies 10 (11):1744. doi:10.3390/en10111744.
   Web of Science ®Google Scholar
• Kirbas, I., and A. Kerem. 2016. Short-term wind speed prediction based on artificial neural network models. Measurement & Control 49 (6):183–90. doi:10.1177/0020294016656891.
   Web of Science ®Google Scholar
• Lan, Y., Y. C. Soh, and G. B. Huang. 2010. Two-stage extreme learning machine for regression. Neurocomputing 73 (16):3028–38. doi:10.1016/j.neucom.2010.07.012.
   Web of Science ®Google Scholar
• Leuenberger, M., and M. Kanevski 2014. “Feature selection in environmental data mining combining simulated annealing and extreme learning machine.” 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 601–06.
   Google Scholar
• Li, C. S., Z. G. Xiao, X. Xia, W. Zou, and C. Zhang. 2018. A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting. Applied Energy 215:131–44. doi:10.1016/j.apenergy.2018.01.094.
   Web of Science ®Google Scholar
• Li, K., and Y. Han. 2018. Modelling for motor load torque with dynamic load changes of beam pumping units based on a serial hybrid model. Transactions of the Institute of Measurement and Control 40 (3):603–917. doi:10.1177/0142331216670454.
   Web of Science ®Google Scholar
• Li, K., Y. Han, and T. Wang. 2018. A novel prediction method for down-hole working conditions of the beam pumping unit based on 8-directions chain codes and online sequential extreme learning machine. Journal of Petroleum Science and Engineering 160:285–301. doi:10.1016/j.petrol.2017.10.052.
   Web of Science ®Google Scholar
• Li, T. Z., Y. X. Li, and X. L. Yang. 2017. Rock burst prediction based on genetic algorithms and extreme learning machine. Journal of Central South University 24 (9):2105–13. doi:10.1007/s11771-017-3619-1.
   Web of Science ®Google Scholar
• Lipu, M. S. H., M. A. Hannan, A. Hussain, and M. H. M. Saad. 2017. Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection. Journal of Renewable & Sustainable Energy 9 (6):064102. doi:10.1063/1.5008491.
   Web of Science ®Google Scholar
• Liu, H., X. W. Mi, and Y. F. Li. 2018. An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm. Renewable Energy 123:694–705. doi:10.1016/j.renene.2018.02.092.
   Web of Science ®Google Scholar
• Ma, X. J., Y. Jin, and Q. L. Dong. 2017. A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting. Applied Soft Computing 54:296–312. doi:10.1016/j.asoc.2017.01.033.
   Web of Science ®Google Scholar
• Niu, B., H. Huang, B. Ye, L. Tan, and J. J. Liang 2014. “Fully learned multi-swarm particle swarm optimization.” 5th International Conference on Swarm Intelligence (ICSI), 150–57. doi:10.1177/1753193414524689
   Google Scholar
• Pu, D. M., D. Q. Gao, T. Ruan, and Y. B. Yuan. 2017. A novel learning algorithm of single-hidden-layer feedforward neural networks. Neural Computing and Applications 28:719–26. doi:10.1007/s00521-016-2372-y.
   Web of Science ®Google Scholar
• Ren, H. J., X. Lei, and P. Zhang. 2016. A study of wind speed prediction based on particle swarm algorithm to optimize the parameters of sparse least squares support vector. International Journal of Simulation: Systems, Science and Technology 17 (30):1–7.
   Google Scholar
• Rong, Y. M., G. J. Zhang, Y. Chang, and Y. Huang. 2016. Integrated optimization model of laser brazing by extreme learning machine and genetic algorithm. International Journal of Advanced Manufacturing Technology 87:2943–50. doi:10.1007/s00170-016-8649-6.
   Web of Science ®Google Scholar
• Santamaria-Bonfil, G., A. Reyes-Ballesteros, and C. Gershenson. 2016. Wind speed forecasting for wind farms: A method based on support vector regression. Renewable Energy 85:790–809. doi:10.1016/j.renene.2015.07.004.
   Web of Science ®Google Scholar
• Song, J. J., J. Z. Wang, and H. Y. Lu. 2018. A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting. Applied Energy 215:643–58. doi:10.1016/j.apenergy.2018.02.070.
   Web of Science ®Google Scholar
• Sun, W., and M. H. Liu. 2016. Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China. Energy Conversion and Management 114:197–208. doi:10.1016/j.enconman.2016.02.022.
   Web of Science ®Google Scholar
• Tian, C. S., and Y. Hao. 2018. A novel nonlinear combined forecasting system for short-term load forecasting. Energies 11 (4):712. doi:10.3390/en11040712.
   Web of Science ®Google Scholar
• Tian, Z. D., S. J. Li, Y. H. Wang, et al. 2017. A prediction method based on wavelet transform and multiple models fusion for chaotic time series. Chaos, Solitons & Fractals 98:158–72. doi:10.1016/j.chaos.2017.03.018.
   Web of Science ®Google Scholar
• Tian, Z. D., S. J. Li, Y. H. Wang, and X. D. Wang. 2018. Wind power prediction method based on hybrid kernel function support vector machine. Wind Engineering 42 (3):252–64. doi:10.1177/0309524X17737337.
   Web of Science ®Google Scholar
• Wang, C. R., R. F. Xu, S. J. Lee, et al. 2018. Network intrusion detection using equality constrained-optimization-based extreme learning machines. Knowledge-Based Systems 147:68–80. doi:10.1016/j.knosys.2018.02.015.
   Web of Science ®Google Scholar
• Wang, J. Z., and J. M. Hu. 2015. 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 93:41–56.
   Web of Science ®Google Scholar
• Wang, J. Z., T. Niu, H. Y. Lu, Z. H. Guo, W. D. Yang, and P. Du. 2018a. An analysis-forecast system for uncertainty modeling of wind speed: A case study of large-scale wind farms. Applied Energy 211:492–512. doi:10.1016/j.apenergy.2017.11.071.
   Web of Science ®Google Scholar
• Wang, J. Z., W. D. Yang, P. Du, and Y. F. Li. 2018b. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system. Energy 148:59–78. doi:10.1016/j.energy.2018.01.112.
   Web of Science ®Google Scholar
• Wang, J. Z., W. D. Yang, P. Du, and T. Niu. 2018c. A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Conversion and Management 163:134–50. doi:10.1016/j.enconman.2018.02.012.
   Web of Science ®Google Scholar
• Wang, W., and R. Zhang 2012. “Improved convex incremental extreme learning machine based on enhanced random search.” Proceedings of the 2012 International Conference on Electrical and Electronics Engineering, 2033–40.
   Google Scholar
• Wang, Y., Z. X. Xie, and Q. H. Hu. 2018. Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning. Energy Conversion and Management 163:384–406. doi:10.1016/j.enconman.2018.02.034.
   Web of Science ®Google Scholar
• Wong, K. I., and P. K. Wong. 2018. Adaptive air-fuel ratio control of dual-injection engines under biofuel blends using extreme learning machine. Energy Conversion & Management 165:66–75. doi:10.1016/j.enconman.2018.03.044.
   Web of Science ®Google Scholar
• Yang, J. X., Y. F. Cheng, and J. T. Huang. 2016. A novel short-term multi-input-multi-output prediction model of wind speed and wind power with LSSVM based on quantum-behaved particle swarm optimization algorithm. Chemical Engineering Transactions 59:871–76.
   Google Scholar
• Yang, W. A., Q. Zhou, and K. L. Tsui. 2016. Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation. International Journal of Production Research 54 (15):4703–21. doi:10.1080/00207543.2015.1111534.
   Web of Science ®Google Scholar
• Yu, C. J., Y. L. Li, H. Y. Xiang, and M. Zhang. 2018. Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and Elman neural network. Journal of Wind Engineering and Industrial Aerodynamics 175:136–43. doi:10.1016/j.jweia.2018.01.020.
   Web of Science ®Google Scholar
• Yu, C. J., Y. L. Li, and M. J. Zhang. 2017. An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on elman neural network. Energy Conversion and Management 148:895–904. doi:10.1016/j.enconman.2017.05.063.
   Web of Science ®Google Scholar