Combined model with secondary decomposition-model selection and sample selection for multi-step wind power forecasting

Du, 2017, Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting, Energy Convers Manage, 150, 90, 10.1016/j.enconman.2017.07.065 Wizelius, 2015 Ouyang, 2016, Wind power prediction method based on regime of switching kernel functions, J Wind Eng Ind Aerodyn, 153, 26, 10.1016/j.jweia.2016.03.005 Global Wind Energy Council. Global wind report 2015; 2015. Milligan M, Kirby B, Acker T, Ahlstom M, Frew B, Goggin M. Review of status of wind integration and transmission in the United States: key issues and lessons learned. 2015. NREL/TP-5D00e61911. Acker T, Pete C. Western wind and solar integration study: hydropower analysis. 2012. NREL/SR-5500e53098. The European Wind Energy Association. Wind in Power: 2015 European Statistics; 2016. Zha, 2016, Selection of time window for wind power ramp prediction based on risk model, Energy Convers Manage, 126, 748, 10.1016/j.enconman.2016.08.064 Jing, 2017, Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method, Appl Energy, 197, 183, 10.1016/j.apenergy.2017.04.017 Wilczak, 2017, NWP Forecast errors of boundary layer flow in complex terrain observed during the Second Wind Forecast Improvement Project (WFIP2) Field Campaign, EGU Gen Assemb Conf Abstracts, 19, 11293 Mughal Muhammad Omer, Lynch Mervyn, Yu Frank, McGann Brendan, Jeanneret Francois, Sutton John. Wind modelling, validation and sensitivity study using Weather Research and Forecasting model in complex terrain. Environ Model Software 2017;90:107–25. ISSN 1364-8152. Soman SS, Zareipour H, Malik O, Mandal P. A review of wind power and wind speed forecasting methods with different time horizons. 2010, September North American power symposium (NAPS)IEEE; 2010. p. 1–8. Erdem, 2011, ARMA based approaches for forecasting the tuple of wind speed and direction, Appl Energy, 88, 1405, 10.1016/j.apenergy.2010.10.031 Wang, 2012, Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China, Energy Policy, 48, 284, 10.1016/j.enpol.2012.05.026 Wang, 2015, Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China, Renew Energy, 76, 91, 10.1016/j.renene.2014.11.011 Santamaría-Bonfil, 2016, Wind speed forecasting for wind farms: a method based on support vector regression, Renew Energy, 85, 790, 10.1016/j.renene.2015.07.004 Li, 2010, On comparing three artificial neural networks for wind speed forecasting, Appl Energy, 87, 2313, 10.1016/j.apenergy.2009.12.013 Guo, 2011, A case study on a hybrid wind speed forecasting method using BP neural network, Knowl. Base Syst., 24, 1048, 10.1016/j.knosys.2011.04.019 Yu, 2017, Comparative study on three new hybrid models using Elman Neural Network and Empirical Mode Decomposition based technologies improved by Singular Spectrum Analysis for hour-ahead wind speed forecasting, Energy Convers Manage, 147, 75, 10.1016/j.enconman.2017.05.008 Wu, 2019, A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting, Appl Energy, 237, 896, 10.1016/j.apenergy.2019.01.046 Xiao, 2016, Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting, Appl Energy, 180, 213, 10.1016/j.apenergy.2016.07.113 Zhou, 2019, Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems, Appl Energy, 250, 1559, 10.1016/j.apenergy.2019.05.016 Bates JM, Granger CWJ. The combination of forecasts. Oper Res Q 1969;20:451–68; Mills TC, editor. Economic forecasting. Edward Elgar; 1999 [Reprinted in]. Xiao, 2017, Research and application of a combined model based on multi-objective optimization for electrical load forecasting, Energy, 119, 1057, 10.1016/j.energy.2016.11.035 Song, 2018, A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting, Appl Energy, 215, 643, 10.1016/j.apenergy.2018.02.070 Hao, 2018, The study and application of a novel hybrid system for air quality early-warning, Appl Soft Comput A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting. Applied Energy, 2019, 235, 5, 5, https://doi.org/10.1016/j.apenergy.2018.11.034. Xiao, 2015, A hybrid model based on data preprocessing for electrical power forecasting, Int J Electr Power Energy Syst, 64, 311, 10.1016/j.ijepes.2014.07.029 Wang, 2018, A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm, Energy Convers Manage, 163, 134, 10.1016/j.enconman.2018.02.012 Xiao, 2017, Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting, Appl Energy, 198, 203, 10.1016/j.apenergy.2017.04.039 Chuanjin, 2017, An improved wavelet transform using singular spectrum analysis for wind speed forecasting based on Elman Neural Network, Energy Convers Manage, 148, 895, 10.1016/j.enconman.2017.05.063 Naik Jyotirmayee, Satapathy Prachitara, Dash PK. Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression. Appl Soft Comput; 2017 [in press]. Xiao, 2017, Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm, Energy Convers Manage, 143, 410, 10.1016/j.enconman.2017.04.012 Liu, 2018, Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks, Energy Convers Manage, 155, 188, 10.1016/j.enconman.2017.10.085 Empirical Gilles, 2013, Transform wavelet, IEEE Trans Signal Process, 61, 3999, 10.1109/TSP.2013.2265222 Liu, 2015, Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks, Appl Energy, 157, 183e194, 10.1016/j.apenergy.2015.08.014 Fildes, 2001, Beyond forecasting competitions, Int J Forecast, 17, 556 Fildes, 2015, Simple versus complex selection rules for forecasting many time series, J Bus Res, 68, 1692, 10.1016/j.jbusres.2015.03.028 Dragomiretskiy, 2014, Variational mode decomposition, IEEE Trans Signal Process, 62, 531, 10.1109/TSP.2013.2288675 Abdoos, 2016, A new intelligent method based on combination of VMD and ELM for short term wind power forecasting, Neurocomputing, 203, 111, 10.1016/j.neucom.2016.03.054 Wang, 2017, Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm, Appl Energy, 190, 390, 10.1016/j.apenergy.2016.12.134 Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci. 454 (1971) (1998) 903–995. Tvedskov, 2013, Cross-validation of three predictive tools for non-sentinel node metastases in breast cancer patients with micrometastases or isolated tumor cells in the sentinel node, Eur J Surg Oncol, 40, 435e441 Shaoa, 2013, Feature selection for manufacturing process monitoring using cross-validation, J Manuf Syst, 32, 550, 10.1016/j.jmsy.2013.05.006 Jiang, 2016, Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation, Neurocomputing, 198, 40, 10.1016/j.neucom.2015.08.118 Santos, 2015, Selectively inhibiting learning bias for active sampling, 62 Dasgupta, 2011, Two faces of active learning, Theor Comput Sci, 412, 1767, 10.1016/j.tcs.2010.12.054 Dasgupta S. Coarse sample complexity bounds for active learning. In: Proceedings of Advances in Neural Information Processing Systems; 2005. p. 235–42. Ye, 2016, Practice makes perfect: an adaptive active learning framework for image classification, Neurocomputing, 196, 95, 10.1016/j.neucom.2016.01.091 Zhang, 2015, Update vs. upgrade: modeling with indeterminate multi-class active learning, Neurocomputing, 162, 163, 10.1016/j.neucom.2015.03.056 Lewis, 1995, A sequential algorithm for training text classifiers: corrigendum and additional data, SIGIR Forum, 29, 13, 10.1145/219587.219592 Settles B. Active learning, synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool Publishers; 2012. Shannon, 1949, Communication theory of secrecy systems, Bell Syst Tech J, 28, 656, 10.1002/j.1538-7305.1949.tb00928.x Xiao, 2015, Combined forecasting models for wind energy forecasting: a case study in China, Renew Sustain Energy Rev, 44, 271, 10.1016/j.rser.2014.12.012 Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126 Kasun, 2013, Representational learning with extreme learning machine for big data, IEEE Intell Syst, 28, 31 Guo, 2000, Evolutionary neural networks based on genetic algorithms, J Tsinghua Univ (Sci Technol), 40, 116 Yang, 1996, The global stability of fuzzy cellular neural networks, IEEE Trans. Circuits Syst, I, 880, 10.1109/81.538999 Huang, 2006, Exponential stability of fuzzy cellular neural networks with distributed delay, Phys Lett A, 351, 48, 10.1016/j.physleta.2005.10.060 Liu, 2009, Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays, Neurocomputing, 72, 1056, 10.1016/j.neucom.2008.04.001 Specht, 1991, A general regression neural network, IEEE Trans Neural Networks, 2, 568, 10.1109/72.97934 Ko, 2013, Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter, Energy, 49, 413, 10.1016/j.energy.2012.11.015 Yang, 2013, Multiobjective flower algorithm for optimization, Proc Comput Sci, 18, 861, 10.1016/j.procs.2013.05.251 Yang XS. Flower pollination algorithm for global optimization. In: Proceedings of the 11th international conference on Unconventional Computation and Natural Computation; 2012. p. 240–9. Zhu, 2018, PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors, Atmos Environ, 183, 20, 10.1016/j.atmosenv.2018.04.004 Huang, 2019, Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China, J Cleaner Prod, 209, 415, 10.1016/j.jclepro.2018.10.128 Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 2007;102(477):359–78. Gneiting T, Katzfuss M. Probabilistic forecasting. Annual Review of Statistics and Its Application 2014;1:125–151.