Short-term wind power prediction based on LSSVM–GSA model
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Yuehua Huang | Qingxiong Tan | Xiaohui Yuan | Chen Chen | Yanbin Yuan | Qingxiong Tan | Xiaohui Yuan | Yanbin Yuan | Yuehua Huang | Cheng Chen
[1] J. Mercer. Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .
[2] Chao Liu,et al. Short-term prediction of wind power using EMD and chaotic theory , 2012 .
[3] X. C. Guo,et al. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization , 2008, Neurocomputing.
[4] Seref Sagiroglu,et al. Data mining and wind power prediction: A literature review , 2012 .
[5] Hossein Nezamabadi-pour,et al. GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..
[6] Aoife Foley,et al. Current methods and advances in forecasting of wind power generation , 2012 .
[7] Lijun Yang,et al. Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation , 2011 .
[8] Sancho Salcedo-Sanz,et al. Short term wind speed prediction based on evolutionary support vector regression algorithms , 2011, Expert Syst. Appl..
[9] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.
[10] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[11] Gaige Wang,et al. Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment , 2013, TheScientificWorldJournal.
[12] Zhansong Wu,et al. Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems , 2011 .
[13] Yongqian Liu,et al. Short-Term Wind-Power Prediction Based on Wavelet Transform–Support Vector Machine and Statistic-Characteristics Analysis , 2011, IEEE Transactions on Industry Applications.
[14] Hamidreza Zareipour,et al. Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm , 2015 .
[15] Clodoaldo Ap. M. Lima,et al. Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study , 2010, Comput. Biol. Medicine.
[16] Hui Liu,et al. Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions , 2015 .
[17] Hongbo Xu,et al. An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO , 2013 .
[18] Seref Sagiroglu,et al. A new approach to very short term wind speed prediction using k-nearest neighbor classification , 2013 .
[19] Fang Liu,et al. Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification , 2015, Knowl. Based Syst..
[20] Jing Shi,et al. Fine tuning support vector machines for short-term wind speed forecasting , 2011 .
[21] Wolf-Gerrit Fruh,et al. Wind forecasting using Principal Component Analysis , 2014 .
[22] Lei Zhang,et al. Comparison of four Adaboost algorithm based artificial neural networks in wind speed predictions , 2015 .
[23] Akin Tascikaraoglu,et al. A review of combined approaches for prediction of short-term wind speed and power , 2014 .
[24] Shigeo Abe,et al. Fuzzy least squares support vector machines for multiclass problems , 2003, Neural Networks.
[25] E. El-Saadany,et al. Grey predictor for wind energy conversion systems output power prediction , 2006, IEEE Transactions on Power Systems.
[26] Huanhuan Chen,et al. Evolving Least Squares Support Vector Machines for Stock Market Trend Mining , 2009, IEEE Trans. Evol. Comput..
[27] George Galanis,et al. Wind power prediction based on numerical and statistical models , 2013 .
[28] Georges Kariniotakis,et al. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. , 2005 .
[29] Maria Grazia De Giorgi,et al. Assessment of the benefits of numerical weather predictions in wind power forecasting based on stati , 2011 .
[30] S. Suresh Kumar,et al. Nonlinear Modeling of a Switched Reluctance Motor using LSSVM - ABC , 2014 .
[31] Robert P. Broadwater,et al. Current status and future advances for wind speed and power forecasting , 2014 .
[32] Nurulkamal Masseran,et al. Markov Chain model for the stochastic behaviors of wind-direction data , 2015 .
[33] Sheng Li,et al. Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm , 2012 .
[34] Chao Chen,et al. An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization , 2013 .
[35] Chao Chen,et al. A hybrid statistical method to predict wind speed and wind power , 2010 .
[36] Mohamed Cheriet,et al. Model selection for the LS-SVM. Application to handwriting recognition , 2009, Pattern Recognit..
[37] José Manuel Benítez,et al. On the use of cross-validation for time series predictor evaluation , 2012, Inf. Sci..
[38] Stéphanie Monjoly,et al. An adaptive short-term prediction scheme for wind energy storage management , 2011 .
[39] M. Lange,et al. Physical Approach to Short-Term Wind Power Prediction , 2005 .
[40] Haiyan Lu,et al. A case study on a hybrid wind speed forecasting method using BP neural network , 2011, Knowl. Based Syst..
[41] Farid Melgani,et al. Kernel ridge regression with active learning for wind speed prediction , 2013 .
[42] Xiaofeng Yang,et al. A hybrid strategy of short term wind power prediction , 2013 .
[43] Jianzhou Wang,et al. A hybrid forecasting approach applied to wind speed time series , 2013 .
[44] M. Ghiasi,et al. Practical use of statistical learning theory for modeling freezing point depression of electrolyte solutions: LSSVM model , 2014 .
[45] Joao P. S. Catalao,et al. A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal , 2011 .
[46] Haiyan Lu,et al. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting , 2014 .
[47] Daniel Ambach,et al. Periodic and long range dependent models for high frequency wind speed data , 2015 .