The main objective of the present study was to develop a
new model for the solar radiation estimation in hilly areas of
North India for the determination of constants ‘a’ and ‘b’ by
taking only latitude and altitude of the place into
consideration. In this study, new model was developed
based on Angstrom-Prescott Model to estimate the monthly
average daily global solar radiation only using sunshine
duration data. The monthly average global solar radiation
data of four different locations in North India was analyzed
with the neural fitting tool (nftool) of neural network of
MATLAB Version 7.11.0.584 (R2010b) with 32-bit (win 32).
The neural network model was used with 10 hidden neurons.
Eight months data was used to train the neural network.
Two months data was used for the validation purpose and
the remaining two months for the testing purpose. The new
developed model estimated the values of ‘a’ which range
from 0.209 to 0.222 and values of ‘b’ ranging from 0.253 to
0.407. The values of maximum percentage error (MPE) and
mean bias error (MBE) were in good agreement with the
actual values. Artificial neural network application showed
that data was best fitted for the regression coefficient of
0.99558 with best validation performance of 0.85906 for Solan.
This will help to advance the state of knowledge of global
solar radiation to the point where it has applications in the
estimation of monthly average daily global solar radiation.
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