Generation of common coefficients to estimate global solar radiation over different locations of India View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-06-08

AUTHORS

Suman Samanta, Pulak Kumar Patra, Saon Banerjee, Lakshmi Narsimhaiah, M. A. Sarath Chandran, P. Vijaya Kumar, Sanjib Bandyopadhyay

ABSTRACT

In developing countries like India, global solar radiation (GSR) is measured at very few locations due to non-availability of radiation measuring instruments. To overcome the inadequacy of GSR measurements, scientists developed many empirical models to estimate location-wise GSR. In the present study, three simple forms of Angstrom equation [Angstrom-Prescott (A-P), Ogelman, and Bahel] were used to estimate GSR at six geographically and climatologically different locations across India with an objective to find out a set of common constants usable for whole country. Results showed that GSR values varied from 9.86 to 24.85 MJ m−2 day−1 for different stations. It was also observed that A-P model showed smaller errors than Ogelman and Bahel models. All the models well estimated GSR, as the 1:1 line between measured and estimated values showed Nash-Sutcliffe efficiency (NSE) values ≥ 0.81 for all locations. Measured data of GSR pooled over six selected locations was analyzed to obtain a new set of constants for A-P equation which can be applicable throughout the country. The set of constants (a = 0.29 and b = 0.40) was named as “One India One Constant (OIOC),” and the model was named as “MOIOC.” Furthermore, the developed constants are validated statistically for another six locations of India and produce close estimation. High R2 values (≥ 76%) along with low mean bias error (MBE) ranging from − 0.64 to 0.05 MJ m−2 day−1 revealed that the new constants are able to predict GSR with lesser percentage of error. More... »

PAGES

943-953

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00704-018-2531-4

DOI

http://dx.doi.org/10.1007/s00704-018-2531-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104476155


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