Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting
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Chen Li | Ping Jiang | Ping Jiang | Chen Li
[1] R. Kavasseri,et al. Day-ahead wind speed forecasting using f-ARIMA models , 2009 .
[2] Dalibor Petkovic,et al. Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique , 2017, Knowledge and Information Systems.
[3] Hasmat Malik,et al. Generalized Regression Neural Network Based Wind Speed Prediction Model for Western Region of India , 2016 .
[4] Jianzhou Wang,et al. A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm , 2018 .
[5] Tingting Zhu,et al. Short-term wind speed forecasting using empirical mode decomposition and feature selection , 2016 .
[6] Teik C. Lim,et al. Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithm-support vector machine , 2016 .
[7] Mostafa Safdari Shadloo,et al. Direct Numerical Simulation of flow instabilities over Savonius style wind turbine blades , 2017 .
[8] Zhang Min,et al. RESEARCH ON PROCESSING OF SHORT-TERM HISTORICAL DATA OF DAILY LOAD BASED ON KALMAN FILTER , 2003 .
[9] Chaomin Luo,et al. Adaptive robust speed control based on recurrent elman neural network for sensorless PMSM servo drives , 2017, Neurocomputing.
[10] Zhijie Zhu,et al. Research and application of a novel hybrid air quality early-warning system: A case study in China. , 2018, The Science of the total environment.
[11] H. J. Lu,et al. An improved neural network-based approach for short-term wind speed and power forecast , 2017 .
[12] Whei-Min Lin,et al. A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems , 2011, IEEE Transactions on Power Electronics.
[13] Dalibor Petković,et al. Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms , 2017 .
[14] Hsing-Chih Tsai,et al. Unified particle swarm delivers high efficiency to particle swarm optimization , 2017, Appl. Soft Comput..
[15] N. D. Hatziargyriou,et al. Probabilistic Wind Power Forecasting Using Radial Basis Function Neural Networks , 2012, IEEE Transactions on Power Systems.
[16] Dalibor Petković,et al. Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation , 2013 .
[17] Hui Liu,et al. Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .
[18] Lei Wu,et al. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .
[19] Meng Hu,et al. Escape of an inertial Lévy flight particle from a truncated quartic potential well , 2017 .
[20] Sonya A. Dehler,et al. Inverse spatial principal component analysis for geophysical survey data interpolation , 2015 .
[21] WenYeau Chang. Application of Back Propagation Neural Network for Wind Power Generation Forecasting , 2013 .
[22] Dalibor Petković,et al. Wind farm efficiency by adaptive neuro-fuzzy strategy , 2016 .
[23] Amin Gholami,et al. Estimation of porosity from seismic attributes using a committee model with bat-inspired optimization algorithm , 2017 .
[24] Haiyan Lu,et al. Combined modeling for electric load forecasting with adaptive particle swarm optimization , 2010 .
[25] Jinyun Guo,et al. One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction , 2017 .
[26] Afshin Ebrahimi,et al. A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm , 2016 .
[27] Ergin Erdem,et al. ARMA based approaches for forecasting the tuple of wind speed and direction , 2011 .
[28] Yu Xue,et al. A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems , 2017, J. Parallel Distributed Comput..
[29] Jun Liang,et al. Short-term wind power combined forecasting based on error forecast correction , 2016 .
[30] Jing Ma,et al. Research and application of a combined model based on multi-objective optimization for electrical load forecasting , 2017 .
[31] Xin-She Yang,et al. New directional bat algorithm for continuous optimization problems , 2017, Expert Syst. Appl..
[32] Shahaboddin Shamshirband,et al. Design and state of art of innovative wind turbine systems , 2016 .
[33] Xi Chen,et al. Intelligent Structural Rating System Based on Backpropagation Network , 2013 .
[34] B. Moreno,et al. A grey neural network and input-output combined forecasting model. Primary energy consumption forecasts in Spanish economic sectors , 2016 .
[35] Maria Fernanda Pimentel,et al. Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. , 2017, Talanta.
[36] Xiaobo Zhang,et al. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm , 2017 .
[37] Pinqi Xia,et al. An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models , 2017 .
[38] Xiao-chao Fan,et al. Comprehensive evaluation index system for wind power utilization levels in wind farms in China , 2017 .
[39] Ahmet Serdar Yilmaz,et al. Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks , 2009, Expert Syst. Appl..
[40] Xu Fan,et al. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting , 2017 .
[41] Chen Wang,et al. Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting , 2017 .
[42] Siti Hafizah Ab Hamid,et al. Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect , 2014, Natural Hazards.
[43] Xi Chen,et al. A hybrid evaluation model for flight performance based on bacterial foraging and Elman network , 2016 .
[44] Bri-Mathias Hodge,et al. Improved Wind Power Forecasting with ARIMA Models , 2011 .
[45] Ali Lahouar,et al. Hour-ahead wind power forecast based on random forests , 2017 .
[46] Chu Zhang,et al. A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting , 2017 .
[47] Joshua Novacheck,et al. Diversifying wind power in real power systems , 2017 .
[48] 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..
[49] Wenyu Zhang,et al. A novel hybrid approach for wind speed prediction , 2014, Inf. Sci..
[50] Souvik Ganguli,et al. Solar and Wind Power Estimation and Economic Load Dispatch Using Firefly Algorithm , 2015, Procedia Computer Science.
[51] Jianzhou Wang,et al. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system , 2018 .
[52] Kequan Zhang,et al. A Novel Combined Model Based on an Artificial Intelligence Algorithm—A Case Study on Wind Speed Forecasting in Penglai, China , 2016 .
[53] Wei Sun,et al. Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China , 2016 .
[54] Yong Peng,et al. Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data , 2017, Comput. Electron. Agric..