Classification of solar radiation zones and general models for estimating the daily global solar radiation on horizontal surfaces in China

Abstract Classification of solar radiation zones constitutes the prerequisite for the establishment of regional daily global solar radiation ( H ) estimation general model. Current zone establishment methods are ordinary based on solar radiation observation stations (SROS) which present a sparse and non-uniform distribution. As a result, the possibility of misclassifications of the stations occurs in cases there are no records of radiation and far away from SROS. Therefore, by using k-means cluster and Support Vector Machine-Genetic Algorithm, a two-step radiation zoning method was proposed in this paper according to: (a) H , sunshine duration, temperature and relative humidity from 98 SROS and (b) sunshine duration, temperature and relative humidity from 562 stations without radiation. The method is capable to combine the SROS and the stations without radiation in the process of classification. Thus, these misclassifications have been effectively reduced and the accuracy of each classification has been significantly improved. Based on the method, five radiation zones have been identified. Concurrently, four sunshine-based models were obtained for each SROS and the analysis of statistical indexes indicated that the cubic models presented the best performances in each zone. According to the best site-specific models and radiation zones, the general models of regional H estimation were developed by introducing the geographical parameters, including latitude and altitude. The comparative results demonstrated that the general models proposed in this paper had better accuracies and can represent the general models for the H estimation of stations without radiation records in China.

[1]  Outlook for Energy Supply and Demand in China , 2013 .

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

[3]  Daniel Nilsson,et al.  Photovoltaic self-consumption in buildings : A review , 2015 .

[4]  Amrita Das,et al.  Estimation of available global solar radiation using sunshine duration over South Korea , 2015 .

[5]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

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

[7]  I. Dincer.  Renewable energy and sustainable development: a crucial review , 2000 .

[8]  Daoliang Li,et al.  A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction , 2013, Math. Comput. Model..

[9]  B. Jamil,et al.  Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: A case study , 2017 .

[10]  Antonio Dumas,et al.  A new correlation between global solar energy radiation and daily temperature variations , 2015 .

[11]  K.G.T. Hollands,et al.  Relationship between sunshine duration and solar radiation , 2013 .

[12]  H. Suehrcke,et al.  On the relationship between duration of sunshine and solar radiation on the earth’s surface: Ångström’s equation revisited , 2000 .

[13]  Michael R. Lyu,et al.  Localized support vector regression for time series prediction , 2009, Neurocomputing.

[14]  Xiaofan Zeng,et al.  Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables , 2015 .

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

[16]  Wei Wu,et al.  A general model for estimation of daily global solar radiation using air temperatures and site geographic parameters in Southwest China , 2013 .

[17]  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 .

[18]  Yi-Ming Wei,et al.  Energy structure, marginal efficiency and substitution rate: An empirical study of China , 2007 .

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

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

[21]  David G. Stork,et al.  Pattern Classification , 1973 .

[22]  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 .

[23]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[24]  Jürgen Greif,et al.  European solar radiation atlas : solar radiation on horizontal and inclined surfaces , 1996 .

[25]  Yang Jianping,et al.  Estimating daily global radiation using two types of revised models in China , 2006 .

[26]  Guillermo P Podesta,et al.  Estimating daily solar radiation in the Argentine Pampas , 2004 .

[27]  K. K. Gopinathan,et al.  A general formula for computing the coefficients of the correlation connecting global solar radiation to sunshine duration , 1988 .

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

[29]  A. Angstroem.  Solar and terrestrial radiation , 1924 .

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

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

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

[33]  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 .

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

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

[36]  F. Garde,et al.  Development of climatic zones and passive solar design in Madagascar , 2009 .

[37]  Qingshan Xu,et al.  Generation of typical solar radiation data for different climates of China , 2012 .

[38]  Sky irradiation mapping , 1993 .

[39]  Jun Qin,et al.  Quality control and estimation of global solar radiation in China , 2010 .

[40]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[41]  A. Maghrabi,et al.  Parameterization of a simple model to estimate monthly global solar radiation based on meteorological variables, and evaluation of existing solar radiation models for Tabouk, Saudi Arabia , 2009 .

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

[43]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[44]  W. Tobler.  On the First Law of Geography: A Reply , 2004 .

[45]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[46]  Isaac Moradi,et al.  Quality control of global solar radiation using sunshine duration hours , 2009 .

[47]  Sheng-De Wang,et al.  Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space , 2009, Pattern Recognit..

[48]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[49]  Cort J. Willmott,et al.  Solar climates of the conterminous united states: A preliminary investigation , 1980 .

[50]  Xiaofan Zeng,et al.  Solar radiation estimation using sunshine hour and air pollution index in China , 2013 .

[51]  Kasra Mohammadi,et al.  A statistical comparative study to demonstrate the merit of day of the year-based models for estimation of horizontal global solar radiation , 2014 .

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

[53]  Hongbin Liu,et al.  General models for estimating daily global solar radiation for different solar radiation zones in mainland China , 2013 .

[54]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[55]  Chris C.S. Lau,et al.  Climate classification and passive solar design implications in China , 2007 .

[56]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[57]  J. Porter,et al.  A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand , 1991 .

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

[59]  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 .

[60]  Lucien Wald,et al.  Solar radiation climate in Africa , 2004 .

[61]  Isaac N. Itodo,et al.  A model for determining the global solar radiation for Makurdi, Nigeria , 2011 .

[62]  E. Taşdemiroǧlu,et al.  A new method for estimating solar radiation from bright sunshine data , 1984 .