Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition

With the steady integration of wind energy into electricity networks, precise wind speed forecasting is an essential element in the administration and management of power systems. However, wind ene...

[1]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[2]  Zhongda Tian,et al.  Chaotic characteristic analysis of short-term wind speed time series with different time scales , 2019, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[3]  M. Powell.  A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation , 1994 .

[4]  Dorota Kurowicka,et al.  Advantages of ARMA-GARCH wind speed time series modeling , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[5]  Mehmet Bilgili,et al.  Wind Speed Prediction of Target Station from Reference Stations Data , 2013 .

[6]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[7]  Yuan Zhao,et al.  A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic , 2020 .

[8]  Jian Wang,et al.  A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm , 2018, Applied Energy.

[9]  Qunli Wu,et al.  Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model , 2019, Sustainability.

[10]  H. Acikgoz,et al.  An extreme learning machine based very short-term wind power forecasting method for complex terrain , 2020 .

[11]  Hongmin Li,et al.  Design of a combined wind speed forecasting system based on decomposition-ensemble and multi-objective optimization approach , 2021 .

[12]  Yanfei Li,et al.  Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm , 2018 .

[13]  Yi-Ming Wei,et al.  An adaptive hybrid model for short term wind speed forecasting , 2020 .

[14]  Chu Zhang,et al.  A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting , 2018 .

[15]  Ping Jiang,et al.  A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting , 2020 .

[16]  Heping Liu,et al.  Comprehensive evaluation of ARMA–GARCH(-M) approaches for modeling the mean and volatility of wind speed , 2011 .

[17]  Michael I. Jordan.  Serial Order: A Parallel Distributed Processing Approach , 1997 .

[18]  T. Rowan.  Functional stability analysis of numerical algorithms , 1990 .

[19]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[20]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[21]  W. C. Miller,et al.  A new acceleration technique for the backpropagation algorithm , 1993, IEEE International Conference on Neural Networks.

[22]  Yanfei Li,et al.  Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM , 2018 .

[23]  Jujie Wang,et al.  Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy , 2018, Applied Energy.

[24]  M. Powell.  The BOBYQA algorithm for bound constrained optimization without derivatives , 2009 .

[25]  M. Steel,et al.  On Bayesian Modelling of Fat Tails and Skewness , 1998 .

[26]  S. Basu,et al.  On the tails of the wind ramp distributions , 2018 .

[27]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[28]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[29]  Hamidreza Zareipour,et al.  A review and discussion of decomposition-based hybrid models for wind energy forecasting applications , 2019, Applied Energy.

[30]  N. L. Johnson,et al.  Systems of frequency curves generated by methods of translation. , 1949, Biometrika.

[31]  Jianming Hu,et al.  A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization , 2018, Applied Energy.

[32]  Ping Jiang,et al.  Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting , 2019, Appl. Soft Comput..

[33]  Gang Wang,et al.  Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM , 2018, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[34]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[35]  Naveed ur Rehman,et al.  Multivariate Variational Mode Decomposition , 2019, IEEE Transactions on Signal Processing.