A novel hybrid system based on multi-objective optimization for wind speed forecasting

Abstract Wind power has demonstrated high-efficiency utilization in electricity system, accordingly, accurate and stable forecasting of wind speed is of vital significance in power grid security management and market economics. However, most former studies only consider either the accuracy or stability, with difficulty achieving the two targets simultaneously, which is insufficient for an effective forecasting method. This paper proposes a novel hybrid forecasting system that includes an effective data decomposition technique, a multi-objective optimization algorithm, a forecasting algorithm, and a set of comprehensive evaluation methods. In this system, the complete ensemble empirical mode decomposition (CEEMD) divides the original wind speed sequence into a set of intrinsic mode functions and then extreme learning machine (ELM) optimized by the multi-objective grey wolf optimization (MOGWO) is applied to achieve excellent forecasting performance. To validate the forecasting performance of the developed forecasting system, wind speed data at 10-min interval collected from Shandong Peninsula, China is considered as case study and comprehensive evaluations are introduced. The results demonstrate that the proposed hybrid system transcends the other compared single and traditional models and simultaneously realizes high accuracy and strong stability. Thus, the proposed CEEMD-MOGWO-ELM system can be effectively and satisfactorily used for smart- grid operation and management.

[1]  Xiaoming Zha,et al.  A combined multivariate model for wind power prediction , 2017 .

[2]  Xu Fan,et al.  A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting , 2017 .

[3]  Jianzhou Wang,et al.  Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting , 2017 .

[4]  Chen Wang,et al.  Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting , 2016 .

[5]  Saad Mekhilef,et al.  Grid-connected isolated PV microinverters: A review , 2017 .

[6]  MirjaliliSeyedali,et al.  Multi-objective grey wolf optimizer , 2016 .

[7]  Ioannis B. Theocharis,et al.  A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation , 2007, Neurocomputing.

[8]  Pradeep Jangir,et al.  Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems , 2016, Applied Intelligence.

[9]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[10]  Tong Niu,et al.  A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network , 2019, Sustainability.

[11]  Sheng-wei Fei,et al.  A hybrid model of EMD and multiple-kernel RVR algorithm for wind speed prediction , 2016 .

[12]  Atul K. Raturi,et al.  Renewables 2016 Global status report , 2015 .

[13]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[14]  Jiannong Cao,et al.  Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems , 2013, IEEE Transactions on Cybernetics.

[15]  Z.A. Bashir,et al.  Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks , 2009, IEEE Transactions on Power Systems.

[16]  Haiyan Lu,et al.  A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series , 2017 .

[17]  Yan Hao,et al.  The study and application of a novel hybrid system for air quality early-warning , 2019, Appl. Soft Comput..

[18]  H. M. I. Pousinho,et al.  An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[19]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[20]  Jianzhou Wang,et al.  A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting , 2019, Applied Energy.

[21]  Fulei Chu,et al.  Non-parametric hybrid models for wind speed forecasting , 2017 .

[22]  Yu Jin,et al.  A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting , 2017, Appl. Soft Comput..

[23]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[24]  Feng Qian,et al.  Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm , 2017 .

[25]  J. Scott Armstrong,et al.  Beyond Accuracy: Comparison of Criteria Used to Select Forecasting Methods , 1995 .

[26]  Chuanjin Yu,et al.  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 , 2017 .

[27]  Lu Cao,et al.  Laplace ℓ1 Huber based cubature Kalman filter for attitude estimation of small satellite , 2018, Acta Astronautica.

[28]  Vera Chung,et al.  Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization , 2014 .

[29]  Hui Liu,et al.  Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms , 2015 .

[30]  Yanfei Li,et al.  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 , 2018 .

[31]  Paolo Mercorelli,et al.  Comments on “Tracking Control of Robotic Manipulators With Uncertain Kinematics and Dynamics” , 2017, IEEE Transactions on Industrial Electronics.

[32]  Lei Zhang,et al.  Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions , 2015 .

[33]  Akin Tascikaraoglu,et al.  A review of combined approaches for prediction of short-term wind speed and power , 2014 .

[34]  Ping Jiang,et al.  A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms , 2016 .

[35]  Jianzhou Wang,et al.  Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China , 2019, Journal of Cleaner Production.

[36]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[37]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[38]  Pradipta Kishore Dash,et al.  Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis , 2018, Expert Syst. Appl..

[39]  George Stavrakakis,et al.  Wind power forecasting using advanced neural networks models , 1996 .

[40]  Jianzhou Wang,et al.  A novel hybrid system based on a new proposed algorithm-Multi-Objective Whale Optimization Algorithm for wind speed forecasting , 2017 .

[41]  Chu Zhang,et al.  Multi-step ahead wind speed forecasting using a hybrid model based on two-stage decomposition technique and AdaBoost-extreme learning machine , 2017 .

[42]  Shahaboddin Shamshirband,et al.  Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine , 2016, Comput. Electron. Agric..

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

[44]  Alfredo Vaccaro,et al.  An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization , 2017 .

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

[46]  Pradipta Kishore Dash,et al.  Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression , 2017, Appl. Soft Comput..

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

[48]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[49]  Chengshi Tian,et al.  A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting , 2019, Applied Energy.

[50]  Sancho Salcedo-Sanz,et al.  Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach , 2014 .

[51]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

[52]  Jianzhou Wang,et al.  A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm , 2018 .

[53]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[54]  Hui Liu,et al.  An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system , 2015 .

[55]  Chuanjin Yu,et al.  An improved Wavelet Transform using Singular Spectrum Analysis for wind speed forecasting based on Elman Neural Network , 2017 .

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

[57]  Jianzhou Wang,et al.  Short-term wind speed forecasting using a hybrid model , 2017 .

[58]  Chengshi Tian,et al.  A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting , 2018 .

[59]  Yufang Wang,et al.  A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting , 2016 .

[60]  Hao Chen,et al.  Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models , 2014 .

[61]  A. Louche,et al.  Forecasting and simulating wind speed in Corsica by using an autoregressive model , 2003 .

[62]  H. J. Lu,et al.  An improved neural network-based approach for short-term wind speed and power forecast , 2017 .

[63]  Zhizhong Wang,et al.  Model optimizing and feature selecting for support vector regression in time series forecasting , 2008, Neurocomputing.