Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China

Abstract Accurate estimation of global solar radiation (Rs) is essential to the design and assessment of solar energy utilization systems. Existing empirical and machine learning models for estimating Rs from sunshine duration were comprehensively reviewed. The performances of 12 empirical model forms and 12 machine learning algorithms for estimating daily Rs were further evaluated in different climatic zones of China as a case study, i.e. the temperate continental zone (TCZ), temperate monsoon zone (TMZ), mountain plateau zone (MPZ) and (sub)tropical monsoon zone (SMZ). The best-performing model at each station and the overall best model for each climatic zone were selected based on six statistical indictors, a global performance index (GPI) and computational costs (computational time and memory usage). The results revealed that the machine learning models (RMSE: 2.055–2.751 MJ m−2 d−1; NRMSE: 12.8–21.3%; R2: 0.839–0.936) generally outperformed the empirical models (RMSE: 2.118–3.540 MJ m−2 d−1; NRMSE: 12.1–27.5%; R2: 0.834–0.935) in terms of prediction accuracy. The cubic model (M3), modified linear-logarithmic model (M5) and power model (M10) attained generally better ranks among empirical models based on GPI. M3 was the top-ranked model in TMZ and MPZ, while general best performance was obtained by M5 and M2 in SMZ and TCZ, respectively. ANFIS, ELM, LSSVM and MARS obtained generally better performance among machine learning models, with the overall best ranking by ANFIS in TCZ and SMZ and by ELM in MPZ and SMZ. XGBoost (8.1 s and 74.2 MB), M5Tree (11.3 s and 29.7 MB), GRNN (12.3 s and 295.3 MB), MARS (14.4 s and 42.6 MB), MLP (22.4 s and 41.3 MB) and ANFIS (29.8 s and 23.1 MB) showed relatively small computational time and memory usage. Comprehensively considering both the prediction accuracy and computational costs, ANFIS is highly recommended, while MARS and XGBoost are also promising models for daily Rs estimation.

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

[2]  Shahaboddin Shamshirband,et al.  The intelligent forecasting of the performances in PV/T collectors based on soft computing method , 2017 .

[3]  Sancho Salcedo-Sanz,et al.  Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach , 2014 .

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

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

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

[7]  Arif Hepbasli,et al.  Comparison of solar radiation correlations for İzmir, Turkey , 2002 .

[8]  M. R. Rietveld,et al.  A new method for estimating the regression coefficients in the formula relating solar radiation to sunshine , 1978 .

[9]  A. Ghanbarzadeh,et al.  The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data , 2010 .

[10]  C. Gueymard.  Parameterized transmittance model for direct beam and circumsolar spectral irradiance , 2001 .

[11]  Nadir Ahmed Elagib,et al.  New approaches for estimating global solar radiation across Sudan , 2000 .

[12]  Shiv O. Prasher,et al.  Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques , 2006 .

[13]  R. Deo,et al.  Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model , 2017, Stochastic Environmental Research and Risk Assessment.

[14]  M. Mohandes.  Modeling global solar radiation using Particle Swarm Optimization (PSO) , 2012 .

[15]  Shahaboddin Shamshirband,et al.  Potential of radial basis function based support vector regression for global solar radiation prediction , 2014 .

[16]  Chigueru Tiba,et al.  Empirical models of daily and monthly global solar irradiation using sunshine duration for Alagoas State, Northeastern Brazil , 2016 .

[17]  Hacer Duzen,et al.  Sunshine-based estimation of global solar radiation on horizontal surface at Lake Van region (Turkey) , 2012 .

[18]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[19]  T. M. Klucher.  Evaluation of models to predict insolation on tilted surfaces , 1978 .

[20]  Fatih Evrendilek,et al.  Spatio-temporal modeling of global solar radiation dynamics as a function of sunshine duration for Turkey , 2007 .

[21]  Wenmin Qin,et al.  Comparison of deterministic and data-driven models for solar radiation estimation in China , 2018 .

[22]  Lifeng Wu,et al.  Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015 , 2016 .

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

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

[25]  David B. Ampratwum,et al.  Estimation of solar radiation from the number of sunshine hours , 1999 .

[26]  K. Bakirci.  Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey , 2009 .

[27]  Hans W. Linderholm,et al.  Observation and calculation of the solar radiation on the Tibetan Plateau , 2012 .

[28]  Ahmet Sarı,et al.  Model selection for global and diffuse radiation over the Central Black Sea (CBS) region of Turkey , 2005 .

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

[30]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

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

[32]  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 .

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

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

[35]  Shahaboddin Shamshirband,et al.  Extreme learning machine for prediction of heat load in district heating systems , 2016 .

[36]  Kaicun Wang,et al.  Measurement Biases Explain Discrepancies between the Observed and Simulated Decadal Variability of Surface Incident Solar Radiation , 2014, Scientific Reports.

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

[38]  Zhengrong Li,et al.  Evaluation of global solar radiation models for Shanghai, China , 2014 .

[39]  Guofeng Wu,et al.  Methods and strategy for modeling daily global solar radiation with measured meteorological data – A case study in Nanchang station, China , 2007 .

[40]  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 .

[41]  Adel Mellit,et al.  Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate , 2016 .

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

[43]  Zekai Şen,et al.  Simple nonlinear solar irradiation estimation model , 2007 .

[44]  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 .

[45]  J. Mubiru,et al.  Assessing the performance of global solar radiation empirical formulations in Kampala, Uganda , 2007 .

[46]  Xin Ma,et al.  Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method , 2016, Neural Computing and Applications.

[47]  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 .

[48]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[49]  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 .

[50]  Rajesh Kumar,et al.  Comparison of regression and artificial neural network models for estimation of global solar radiations , 2015 .

[51]  F. Valero,et al.  A single method to estimate the daily global solar radiation from monthly data , 2015 .

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

[53]  O. Kisi.  Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree , 2015 .

[54]  Zhou Jin,et al.  General formula for estimation of monthly average daily global solar radiation in China , 2005 .

[55]  Ozgur Kisi,et al.  Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree , 2018 .

[56]  Haydar Demirhan,et al.  A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation , 2017 .

[57]  Min-Yuan Cheng,et al.  Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines , 2014, Appl. Soft Comput..

[58]  Chandra A. Madramootoo,et al.  Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada , 2014, Theoretical and Applied Climatology.

[59]  F. J. Newland,et al.  A study of solar radiation models for the coastal region of South China , 1989 .

[60]  I. T. Toğrul,et al.  Estimation of monthly global solar radiation from sunshine duration measurement in Elaziğ , 2000 .

[61]  I. T. Toğrul,et al.  Global solar radiation over Turkey: comparison of predicted and measured data , 2002 .

[62]  A. Angstrom.  Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation , 2007 .

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

[64]  Shahaboddin Shamshirband,et al.  Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran , 2015 .

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

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

[67]  O. Kisi.  The potential of different ANN techniques in evapotranspiration modelling , 2008 .

[68]  Efim G. Evseev,et al.  The assessment of different models to predict the global solar radiation on a surface tilted to the south , 2009 .

[69]  Paul Gravila,et al.  Functional fuzzy approach for forecasting daily global solar irradiation , 2012 .

[70]  H. Cai,et al.  Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China , 2018, Agricultural and Forest Meteorology.

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

[72]  Shahaboddin Shamshirband,et al.  A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation , 2015 .

[73]  A. Mellit,et al.  An ANFIS-based Forecasting for Solar Radiation Data from Sunshine Duration and Ambient Temperature , 2007, 2007 IEEE Power Engineering Society General Meeting.

[74]  Y. Li,et al.  A cloud-based reconstruction of surface solar radiation trends for Australia , 2008 .

[75]  Saad Mekhilef,et al.  Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems , 2017 .

[76]  Marc Muselli,et al.  Satellite-based assessment and in situ validation of solar irradiation maps in the Republic of Djibouti , 2015 .

[77]  Shunlin Liang,et al.  Estimation of high-resolution land surface net shortwave radiation from AVIRIS data: Algorithm development and preliminary results , 2015 .

[78]  Yun-Ze Li,et al.  Investigation of heat transfer mechanism of low environmental pressure large-space spray cooling for near-space flight systems , 2018 .

[79]  Basharat Jamil,et al.  Statistical analysis of sunshine based global solar radiation (GSR) models for tropical wet and dry climatic Region in Nagpur, India: A case study , 2018 .

[80]  Adel Mellit,et al.  Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study , 2012 .

[81]  R. Deo,et al.  Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland , 2017 .

[82]  Jing Huang,et al.  An analytical comparison of four approaches to modelling the daily variability of solar irradiance using meteorological records , 2014 .

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

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

[85]  A. H. Maghrabi,et al.  Sunshine-based global radiation models: A review and case study , 2014 .

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

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

[88]  Ali Naci Celik,et al.  A critical review on the estimation of daily global solar radiation from sunshine duration , 2006 .

[89]  Shahaboddin Shamshirband,et al.  A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran , 2017 .

[90]  A. Katiyar,et al.  Simple correlation for estimating the global solar radiation on horizontal surfaces in India , 2010 .

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

[92]  Lifeng Wu,et al.  Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature , 2018, Renewable and Sustainable Energy Reviews.

[93]  Shervin Motamedi,et al.  A hybrid SVM-FFA method for prediction of monthly mean global solar radiation , 2015, Theoretical and Applied Climatology.

[94]  O. D. Ohijeagbon,et al.  New model to estimate daily global solar radiation over Nigeria , 2014 .

[95]  María Amparo Gilabert,et al.  Mapping daily global solar irradiation over Spain: A comparative study of selected approaches , 2011 .

[96]  Intikhab Ulfat,et al.  Empirical Models for the Correlation of Monthly Average Daily Global Solar Radiation with Hours of Sunshine on a Horizontal Surface at Karachi, Pakistan , 2004 .

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

[98]  Liang Zhao,et al.  Global solar radiation estimation with sunshine duration in Tibet, China , 2011 .

[99]  Mehdi Mehrpooya,et al.  A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study , 2018 .

[100]  Viorel Badescu,et al.  Simple solar radiation modelling for different cloud types and climatologies , 2015, Theoretical and Applied Climatology.

[101]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

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

[104]  J. Friedman.  Stochastic gradient boosting , 2002 .

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

[106]  Junliang Fan,et al.  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 , 2018 .

[107]  Kaicun Wang,et al.  Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets , 2018, Remote. Sens..

[108]  Majid Jamil,et al.  Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters , 2014 .

[109]  Ahmet Teke,et al.  Estimating the monthly global solar radiation for Eastern Mediterranean Region , 2014 .

[110]  O. Kisi,et al.  Comparison of Gene Expression Programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain) , 2012 .

[111]  Kamaruzzaman Sopian,et al.  A review of solar energy modeling techniques , 2012 .

[112]  Ozgur Kisi,et al.  Prediction of solar radiation in China using different adaptive neuro‐fuzzy methods and M5 model tree , 2017 .

[113]  Michael F. Modest,et al.  Full-Spectrum Correlated-k Distribution for Shortwave Atmospheric Radiative Transfer , 2004 .

[114]  M. Guermoui,et al.  Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate , 2018, The European Physical Journal Plus.

[115]  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 .

[116]  Faicel Hnaien,et al.  Estimation of Global Solar Radiation Using Three Simple Methods , 2013 .

[117]  Kasra Mohammadi,et al.  Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran , 2013 .

[118]  Abbas Rohani,et al.  A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I) , 2018 .

[119]  A. A. El-Sebaii,et al.  Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia , 2010 .

[120]  V. Bahel,et al.  A correlation for estimation of global solar radiation , 1987 .

[121]  Jiankai Wang,et al.  Decadal variability of surface incident solar radiation over China: Observations, satellite retrievals, and reanalyses , 2015 .

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

[123]  Chuan Ding,et al.  Prioritizing Influential Factors for Freeway Incident Clearance Time Prediction Using the Gradient Boosting Decision Trees Method , 2017, IEEE Transactions on Intelligent Transportation Systems.