Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy

Big multi-step wind speed forecasting is hard to be realized due to the high -requirement of the built forecasting models. However, the big multi-step forecasting is expected in the wind power systems, which can provide sufficient time for the wind grids to be operated in the emergency cases. In the study, a new hybrid computational framework for the big multi-step wind speed forecasting is proposed, consisting of Wavelet Packet Decomposition (WPD), Elman Neural Networks (ENN), boosting algorithms and Wavelet Packet Filter (WPF). The novelty of the study is to investigate the big multi-step wind speed forecasting performance using various computing strategies in the proposed new hybrid WPD-Boost-ENN-WPF framework. Four different wind speed time series data are provided to complete the real forecasting experiments. The experimental results indicate that: (a) all of the proposed hybrid models have better performance than the corresponding single forecasting models in the big multi-step predictions. The 9 step MAE errors for the experimental data #1 from the proposed four hybrid forecasting models are only 1.2821 m/s, 1.1276 m/s, 1.1718 m/s and 1.2684 m/s, respectively; (b) the proposed four hybrid forecasting models have no significant forecasting difference; and (c) all of them are suitable for the big multi-step wind speed forecasting.

[1]  Ranjeeta Bisoi,et al.  Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression , 2018, Renewable Energy.

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

[3]  Ayhan Demiriz,et al.  Linear Programming Boosting via Column Generation , 2002, Machine Learning.

[4]  F. Cassola,et al.  Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output , 2012 .

[5]  Cheng Liu,et al.  Research and application of ensemble forecasting based on a novel multi-objective optimization algorithm for wind-speed forecasting , 2017 .

[6]  Jianzhou Wang,et al.  Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy , 2018 .

[7]  Adel Nasiri,et al.  A multi‐predictor model to estimate solar and wind energy generations , 2018 .

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

[9]  Olivier Grunder,et al.  Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction , 2017 .

[10]  Andreas S. Weigend,et al.  Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .

[11]  Hui Liu,et al.  A novel ensemble model of different mother wavelets for wind speed multi-step forecasting , 2018, Applied Energy.

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

[13]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[14]  Haiping Wu,et al.  Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction , 2018 .

[15]  T. Y. Ji,et al.  Ultra-short-term forecast of wind speed and wind power based on morphological high frequency filter and double similarity search algorithm , 2019, International Journal of Electrical Power & Energy Systems.

[16]  Essam H. Houssein,et al.  Particle Swarm Optimization-Enhanced Twin Support Vector Regression for Wind Speed Forecasting , 2019, J. Intell. Syst..

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

[18]  Andrzej M. Trzynadlowski,et al.  Wind speed and wind direction forecasting using echo state network with nonlinear functions , 2019, Renewable Energy.

[19]  Yulei Rao,et al.  A deep learning framework for financial time series using stacked autoencoders and long-short term memory , 2017, PloS one.

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

[21]  Dale Schuurmans,et al.  Boosting in the Limit: Maximizing the Margin of Learned Ensembles , 1998, AAAI/IAAI.

[22]  Homayoun Najjaran,et al.  Adaboost.MRT: Boosting regression for multivariate estimation , 2014, Artif. Intell. Res..

[23]  Gianluca Bontempi,et al.  Conditionally dependent strategies for multiple-step-ahead prediction in local learning , 2011 .

[24]  Durga L. Shrestha,et al.  Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression , 2006, Neural Computation.

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

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

[27]  Kung-Sik Chan,et al.  Time Series Analysis: With Applications in R , 2010 .

[28]  Jianzhou Wang,et al.  Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China , 2015 .

[29]  D. M. Vinod Kumar,et al.  Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction , 2018, Energy Conversion and Management.

[30]  Zhongyi Hu,et al.  Beyond One-Step-Ahead Forecasting: Evaluation of Alternative Multi-Step-Ahead Forecasting Models for Crude Oil Prices , 2013, ArXiv.

[31]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[32]  Asifullah Khan,et al.  Wind power prediction using deep neural network based meta regression and transfer learning , 2017, Appl. Soft Comput..

[33]  Haiping Wu,et al.  Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction , 2018, Energy Conversion and Management.

[34]  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..

[35]  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 .

[36]  Nurulkamal Masseran.  Modeling the fluctuations of wind speed data by considering their mean and volatility effects , 2016 .

[37]  Hui Liu,et al.  Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine , 2019, Energy Conversion and Management.

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

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

[40]  Hui Liu,et al.  Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network , 2018 .

[41]  Zhi-Hua Zhou,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .

[42]  Junlin Zhou,et al.  Improved Boosting Algorithm Using Combined Weak Classifiers , 2011 .

[43]  Jianzhou Wang,et al.  A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts , 2017 .

[44]  Sinvaldo Rodrigues Moreno,et al.  Wind speed forecasting approach based on Singular Spectrum Analysis and Adaptive Neuro Fuzzy Inference System , 2017, Renewable Energy.

[45]  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.

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

[47]  Sancho Salcedo-Sanz,et al.  Short term wind speed prediction based on evolutionary support vector regression algorithms , 2011, Expert Syst. Appl..

[48]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

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

[50]  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.

[51]  Chen Jie,et al.  Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization , 2018, Energy Conversion and Management.

[52]  Jian Wang,et al.  A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction , 2017, Environmental research.

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

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

[55]  Jianzhou Wang,et al.  Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models , 2016 .

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

[57]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .