Multi-Step Short-Term Wind Power Prediction Based on Three-level Decomposition and Improved Grey Wolf Optimization

Wind power prediction is of great importance in enhancing wind energy penetration. This paper proposes a novel wind power prediction method which combining three-level decomposition with optimized prediction method. In the decomposition part, the Wavelet Packet Decomposition (WPD) is introduced as the first level decomposition, then the obtained sub-series are further decomposed by Variable Mode Decomposition (VMD). At last, Singular Spectrum Analysis (SSA) is carried out for each Intrinsic Mode Function (IMF), and the dominant component and residual components are separated as the input of the prediction. In the prediction part, Kernel Extreme Learning Machine (KELM) is adopted to complete the multi-steps wind power prediction. In this paper, an Improved Grey Wolf Optimization (IGWO) algorithm with redesign of the hierarchy and architecture is proposed, which especially suitable for optimizing wind power prediction. Finally, ten different models are compared, and the results show that the proposed method in this paper can extract the trend information of wind power greatly and has achieved excellent accuracy in short-term wind power prediction.

[1]  Venkata Dinavahi,et al.  Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed , 2018, IEEE Access.

[2]  Yanfei Li,et al.  An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm , 2018 .

[3]  Tian Zhongda.  Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump , 2020 .

[4]  Sujit Kumar Dash,et al.  Short Term Wind Power Forecasting using Hybrid Variational Mode Decomposition and Multi-Kernel Regularized Pseudo Inverse Neural Network , 2018 .

[5]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[6]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  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.

[8]  Mohammad Salay Naderi,et al.  A New Combinatory Approach for Wind Power Forecasting , 2020, IEEE Systems Journal.

[9]  C. Y. Chung,et al.  Novel Multi-Step Short-Term Wind Power Prediction Framework Based on Chaotic Time Series Analysis and Singular Spectrum Analysis , 2018, IEEE Transactions on Power Systems.

[10]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[11]  Birgitte Bak-Jensen,et al.  ARIMA-Based Time Series Model of Stochastic Wind Power Generation , 2010, IEEE Transactions on Power Systems.

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

[13]  Kai Wang,et al.  Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM , 2019, Energy Conversion and Management.

[14]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[15]  Ali Akbar Abdoos,et al.  A new intelligent method based on combination of VMD and ELM for short term wind power forecasting , 2016, Neurocomputing.

[16]  Bo Wang,et al.  A novel clustering algorithm based on mathematical morphology for wind power generation prediction , 2019 .

[17]  Joao P. S. Catalao,et al.  Improved EMD-Based Complex Prediction Model for Wind Power Forecasting , 2020, IEEE Transactions on Sustainable Energy.

[18]  Xiangdong Wang,et al.  Wind power prediction method based on hybrid kernel function support vector machine , 2017 .

[19]  Gang Wang,et al.  Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction , 2018, Wind Engineering.

[20]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Yanhong Wang,et al.  A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed , 2020 .

[22]  Chao Zhang,et al.  An Improved ELM Model Based on CEEMD-LZC and Manifold Learning for Short-Term Wind Power Prediction , 2019, IEEE Access.

[23]  Chen Wang,et al.  Particle-swarm optimization of ensemble neural networks with negative correlation learning for forecasting short-term wind speed of wind farms in western China , 2019, Inf. Sci..

[24]  M. Lange,et al.  Physical Approach to Short-Term Wind Power Prediction , 2005 .

[25]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[26]  Wang Yanhong,et al.  A prediction method based on wavelet transform and multiple models fusion for chaotic time series , 2017 .

[27]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[28]  Gang Wang,et al.  AN ADAPTIVE ONLINE SEQUENTIAL EXTREME LEARNING MACHINE FOR SHORT-TERM WIND SPEED PREDICTION BASED ON IMPROVED ARTIFICIAL BEE COLONY ALGORITHM , 2018 .

[29]  Yi Zhang,et al.  Wind Power Prediction Based on PSO-SVR and Grey Combination Model , 2019, IEEE Access.

[30]  Nima Amjady,et al.  Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine , 2017 .

[31]  Pinar Karagoz,et al.  Data Mining-Based Upscaling Approach for Regional Wind Power Forecasting: Regional Statistical Hybrid Wind Power Forecast Technique (RegionalSHWIP) , 2019, IEEE Access.

[32]  Ang Li,et al.  A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches , 2019, Energies.

[33]  Jing Shi,et al.  Fine tuning support vector machines for short-term wind speed forecasting , 2011 .

[34]  Jian Weng,et al.  A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM , 2019, IEEE Internet of Things Journal.

[35]  Eunsung Oh,et al.  Reinforcement-Learning-Based Energy Storage System Operation Strategies to Manage Wind Power Forecast Uncertainty , 2020, IEEE Access.

[36]  Nilay Shah,et al.  Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression , 2019, Renewable and Sustainable Energy Reviews.

[37]  Jianhua Gu,et al.  Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy , 2019, Expert Syst. Appl..

[38]  Hui Liu,et al.  Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction , 2012 .

[39]  Ergin Erdem,et al.  ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .

[40]  Raymond R. Tan,et al.  Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm , 2020, Journal of Cleaner Production.

[41]  Hui Liu,et al.  Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm , 2017 .

[42]  Yachao Zhang,et al.  A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing , 2019, Energy.