Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation
暂无分享,去创建一个
Mumtaz Ali | Huma Khan | Paul Kwan | Ramendra Prasad | R. Prasad | Mumtaz Ali | P. Kwan | Huma Khan
[1] Zhenyu Wang,et al. Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition , 2018 .
[2] Ahmad Zahedi,et al. Australian renewable energy progress , 2010 .
[3] Junfei Chen,et al. Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast , 2012 .
[4] C. Willmott. Some Comments on the Evaluation of Model Performance , 1982 .
[5] Saleh M. Al-Alawi,et al. An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation , 1998 .
[6] C. Willmott. ON THE VALIDATION OF MODELS , 1981 .
[7] Maysam F. Abbod,et al. Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia , 2013, Entropy.
[8] Shou-yu Chen,et al. Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD , 2013 .
[9] Adel Mellit,et al. Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study , 2012 .
[10] D. Bertsimas,et al. Moment Problems and Semidefinite Optimization , 2000 .
[11] Holger R. Maier,et al. Future research challenges for incorporation of uncertainty in environmental and ecological decision-making , 2008 .
[12] Nilesh Kumar,et al. Prediction of Solar Energy Based on Intelligent ANN Modeling , 2016, International Journal of Renewable Energy Research.
[13] Ben Chie Yen. Discussion and Closure: Criteria for Evaluation of Watershed Models , 1995 .
[14] Robert J. Abrahart,et al. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts , 2007, Environ. Model. Softw..
[15] María Eugenia Torres,et al. Improved complete ensemble EMD: A suitable tool for biomedical signal processing , 2014, Biomed. Signal Process. Control..
[16] Wai Yan Nyein Naing. Forecasting of monthly temperature variations using random forests , 2015 .
[17] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[18] Francesco Maffioli,et al. Coloured Ant System and Local Search to Design Local Telecommunication Networks , 2001, EvoWorkshops.
[19] O. Kisi. Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree , 2015 .
[20] Mehmet Şahin,et al. Application of extreme learning machine for estimating solar radiation from satellite data , 2014 .
[21] S. Adarsh,et al. Scale dependent prediction of reference evapotranspiration based on Multi-Variate Empirical mode decomposition , 2017, Ain Shams Engineering Journal.
[22] Yan Li,et al. Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition , 2018, Geoderma.
[23] I. Moore,et al. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .
[24] 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.
[25] Q. Ouyang,et al. Monthly Rainfall Forecasting Using EEMD-SVR Based on Phase-Space Reconstruction , 2016, Water Resources Management.
[26] D. Legates,et al. Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .
[27] D. P. Mandic,et al. Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[28] Ali Lahouar,et al. Hour-ahead wind power forecast based on random forests , 2017 .
[29] Jan Adamowski,et al. Wavelet‐based multiscale performance analysis: An approach to assess and improve hydrological models , 2014 .
[30] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[31] Nasrudin Abd Rahim,et al. A review on global solar energy policy , 2011 .
[32] 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).
[33] Michalis E. Zervakis,et al. Solar Radiation Time-Series Prediction Based on Empirical Mode Decomposition and Artificial Neural Networks , 2014, AIAI.
[34] Danilo P. Mandic,et al. Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis , 2013, IEEE Signal Processing Magazine.
[35] Sancho Salcedo-Sanz,et al. An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia , 2018 .
[36] R. Deo,et al. Very short‐term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle , 2017, Environmental research.
[37] G. Di Caro,et al. Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[38] Yongqiu Xia,et al. Multivariate Empirical Mode Decomposition Derived Multi-Scale Spatial Relationships between Saturated Hydraulic Conductivity and Basic Soil Properties , 2015 .
[39] Dorothy Ndedi Monekosso,et al. A review of ant algorithms , 2009, Expert Syst. Appl..
[40] Volmir Eugênio Wilhelm,et al. Short-term solar radiation forecasting by using an iterative combination of wavelet artificial neural networks , 2016 .
[41] P. N. Suganthan,et al. A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods , 2015, IEEE Transactions on Sustainable Energy.
[42] D. Verdon‐Kidd,et al. On the uncertainties associated with using gridded rainfall data as a proxy for observed , 2011 .
[43] Lars Imsland,et al. Forecasting using multivariate empirical mode decomposition — Applied to iceberg drift forecast , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).
[44] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[45] T. Stützle,et al. A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends , 2002 .
[46] Ying-Yi Hong,et al. Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition , 2013 .
[47] Kin Keung Lai,et al. Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price , 2016 .
[48] Gregory Z. Grudic,et al. A Formulation for Minimax Probability Machine Regression , 2002, NIPS.
[49] Karoro Angela,et al. Predicting global solar radiation using an artificial neural network single-parameter model , 2011 .
[50] Danilo P. Mandic,et al. Multiscale Image Fusion Using Complex Extensions of EMD , 2009, IEEE Transactions on Signal Processing.
[51] José R. Dorronsoro,et al. Hybrid machine learning forecasting of solar radiation values , 2016, Neurocomputing.
[52] J. Deneubourg,et al. The self-organizing exploratory pattern of the argentine ant , 1990, Journal of Insect Behavior.
[53] B. Sivaneasan,et al. Solar Forecasting using ANN with Fuzzy Logic Pre-processing , 2017 .
[54] John O. Carter,et al. Using spatial interpolation to construct a comprehensive archive of Australian climate data , 2001, Environ. Model. Softw..
[55] Christian Blum,et al. ACO Applied to Group Shop Scheduling: A Case Study on Intensification and Diversification , 2002, Ant Algorithms.
[56] Ahmed Fahmy,et al. A proof of convergence for Ant algorithms , 2004, Inf. Sci..
[57] Danilo P. Mandic,et al. Filter Bank Property of Multivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Signal Processing.
[58] Farid Melgani,et al. Multi-step ahead forecasting of daily global and direct solar radiation: A review and case study of Ghardaia region , 2018, Journal of Cleaner Production.
[59] Dimitri P. Solomatine,et al. Neural networks and M5 model trees in modelling water level-discharge relationship , 2005, Neurocomputing.
[60] Ali Rahimikhoob,et al. A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region , 2013, Water Resources Management.
[61] Kenneth Wong,et al. Improved historical solar radiation gridded data for Australia , 2013, Environ. Model. Softw..
[62] Andy Liaw,et al. Classification and Regression by randomForest , 2007 .
[63] J. R. Quinlan. Learning With Continuous Classes , 1992 .
[64] Christian A. Gueymard,et al. Minimum redundancy – Maximum relevance with extreme learning machines for global solar radiation forecasting: Toward an optimized dimensionality reduction for solar time series , 2017 .
[65] Wei Hu,et al. Soil water prediction based on its scale-specific control using multivariate empirical mode decomposition , 2013 .
[66] Laurel Saito,et al. ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment , 2017 .
[67] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[68] J. Zajaczkowski. A comparison of the BAWAP and SILO spatially interpolated daily rainfall datasets , 2009 .
[69] Norden E. Huang,et al. Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..
[70] Yi Lin,et al. Random Forests and Adaptive Nearest Neighbors , 2006 .
[71] Ramón Díaz-Uriarte,et al. Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.
[72] R. Deo,et al. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm , 2017 .
[73] Olli Bräysy,et al. A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows , 2003, INFORMS J. Comput..
[74] X. Wen,et al. A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .
[75] Rasool Azimi,et al. A novel soft computing framework for solar radiation forecasting , 2016, Appl. Soft Comput..
[76] J DhaliaSweetlin,et al. Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images , 2017, Comput. Methods Programs Biomed..
[77] Norden E. Huang,et al. On the time-varying trend in global-mean surface temperature , 2011 .
[78] He Jiang,et al. A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation , 2016 .
[79] Gurpreet Singh Bhamra,et al. Ant colony algorithms in MANETs: A review , 2012, J. Netw. Comput. Appl..
[80] Gary A. Peterson,et al. Soil Attribute Prediction Using Terrain Analysis , 1993 .