Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China

[1]  Elie Azar,et al.  Evaluation of tree-based ensemble learning algorithms for building energy performance estimation , 2018 .

[2]  Lifeng Wu,et al.  Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions , 2018 .

[3]  A. Selvakumar,et al.  Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters , 2017, Renewable Energy.

[4]  J. Mikulović,et al.  The empirical models for estimating solar insolation in Serbia by using meteorological data on cloudiness , 2017 .

[5]  Ningbo Cui,et al.  Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. , 2017 .

[6]  Nwokolo Samuel Chukwujindu.  A comprehensive review of empirical models for estimating global solar radiation in Africa , 2017 .

[7]  S. Kaseb,et al.  Potential of four different machine-learning algorithms in modeling daily global solar radiation , 2017 .

[8]  Muhammed A. Hassan,et al.  Exploring the potential of tree-based ensemble methods in solar radiation modeling , 2017 .

[9]  Ouarda Assas,et al.  Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria) , 2017 .

[10]  Basharat Jamil,et al.  Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India , 2017 .

[11]  J. Antonanzas,et al.  Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain , 2017 .

[12]  Yagob Dinpashoh,et al.  Evaluation and development of empirical models for estimating daily solar radiation , 2017 .

[13]  Ling Zou,et al.  Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems , 2017 .

[14]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[15]  S. Deng,et al.  A critical review of the models used to estimate solar radiation , 2017 .

[16]  Laurel Saito,et al.  ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment , 2017 .

[17]  Zhijian Liu,et al.  Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: A combined modeling and experimental study , 2017 .

[18]  Laurel Saito,et al.  Estimating daily global solar radiation by day of the year in six cities located in the Yucatán Peninsula, Mexico , 2017 .

[19]  Andy Liaw,et al.  Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships , 2016, J. Chem. Inf. Model..

[20]  Basharat Jamil,et al.  Estimation of solar radiation and optimum tilt angles for south-facing surfaces in Humid Subtropical Climatic Region of India , 2016 .

[21]  Vahid Nourani,et al.  Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches , 2016 .

[22]  Gasser E. Hassan,et al.  New Temperature-based Models for Predicting Global Solar Radiation , 2016 .

[23]  Weiliang Fan,et al.  A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products , 2016 .

[24]  O. Kisi,et al.  Solar radiation prediction using different techniques: model evaluation and comparison , 2016 .

[25]  Faisal Saeed,et al.  Bioactive Molecule Prediction Using Extreme Gradient Boosting , 2016, Molecules.

[26]  X. Wen,et al.  A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .

[27]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[28]  O. Kisi,et al.  Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution , 2016 .

[29]  Laurel Saito,et al.  Empirical models for estimating daily global solar radiation in Yucatán Peninsula, Mexico , 2016 .

[30]  Milan Despotovic,et al.  Review and statistical analysis of different global solar radiation sunshine models , 2015 .

[31]  Yusuf Al-Turki,et al.  Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study , 2015 .

[32]  Saad Mekhilef,et al.  Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria , 2015 .

[33]  Ahmet Teke,et al.  Evaluation and performance comparison of different models for the estimation of solar radiation , 2015 .

[34]  Kasra Mohammadi,et al.  A support vector machine–firefly algorithm-based model for global solar radiation prediction , 2015 .

[35]  C. W. Tong,et al.  A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation , 2015 .

[36]  Khubaib Amjad Alam,et al.  Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .

[37]  Taqiy Eddine Boukelia,et al.  Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria) , 2014 .

[38]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[39]  A. Mellit,et al.  A simplified calibrated model for estimating daily global solar radiation in Madinah, Saudi Arabia , 2014, Theoretical and Applied Climatology.

[40]  Adel Mellit,et al.  New combined models for estimating daily global solar radiation from measured air temperature in semi-arid climates: Application in Ghardaïa, Algeria , 2014 .

[41]  Jilong Chen,et al.  Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables , 2014, Theoretical and Applied Climatology.

[42]  Francisco Javier Martinez-de-Pison,et al.  Evaluation and improvement of empirical models of global solar irradiation: Case study northern Spain , 2013 .

[43]  Kasra Mohammadi,et al.  Prediction of daily global solar radiation by day of the year in four cities located in the sunny regions of Iran , 2013 .

[44]  Shengjun Wu,et al.  Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration , 2013 .

[45]  F. Besharat,et al.  Empirical models for estimating global solar radiation: A review and case study , 2013 .

[46]  T. R. Sumithira,et al.  Prediction of monthly global solar radiation using adaptive neuro fuzzy inference system (ANFIS) technique over the State of Tamilnadu (India): a comparative study , 2012 .

[47]  Wei Wu,et al.  Assessment of monthly solar radiation estimates using support vector machines and air temperatures , 2012 .

[48]  Marta Benito,et al.  Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain) , 2011 .

[49]  Serm Janjai,et al.  Estimation of solar radiation over Cambodia from long-term satellite data , 2011 .

[50]  Huashan Li,et al.  Estimating daily global solar radiation by day of year in China , 2010 .

[51]  Kadir Bakirci,et al.  Models of solar radiation with hours of bright sunshine: A review , 2009 .

[52]  Xurong Mei,et al.  Evaluation of temperature-based global solar radiation models in China , 2009 .

[53]  Xibin Ji,et al.  New methods to estimate global radiation based on meteorological data in China , 2006 .

[54]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[55]  Wenzhi Zhao,et al.  Validation of five global radiation models with measured daily data in China , 2004 .

[56]  Javier Almorox,et al.  Global solar radiation estimation using sunshine duration in Spain , 2004 .

[57]  Viorel Badescu,et al.  A new kind of cloudy sky model to compute instantaneous values of diffuse and global solar irradiance , 2002 .

[58]  Júlio Tóta da Silva,et al.  Cloud modulation of surface solar irradiance at a pasture site in southern Brazil , 2001 .

[59]  De Li Liu,et al.  Estimation of solar radiation in Australia from rainfall and temperature observations , 2001 .

[60]  Zohrab Samani,et al.  Estimating solar radiation and evapotranspiration using minimum climatological data , 2000 .

[61]  George H. Hargreaves,et al.  Irrigation Water Requirements for Senegal River Basin , 1985 .

[62]  G. Campbell,et al.  On the relationship between incoming solar radiation and daily maximum and minimum temperature , 1984 .

[63]  Z. Samani,et al.  Estimating Potential Evapotranspiration , 1982 .

[64]  Hilmi Cenk Bayrakçi,et al.  The development of empirical models for estimating global solar radiation on horizontal surface: A case study , 2018 .

[65]  Lifeng Wu,et al.  New combined models for estimating daily global solar radiation based on sunshine duration in humid regions: A case study in South China , 2018 .

[66]  Kadir Bakirci,et al.  Prediction of global solar radiation and comparison with satellite data , 2017 .

[67]  Shengjun Wu,et al.  Assessing the transferability of support vector machine model for estimation of global solar radiation from air temperature , 2015 .

[68]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.