Single Step Ahead Assessment Of Solar Irradiation Using Ann Model Based On Various Combination Of Meterological Parameter

Abstract

Solar energy is a valuable resource on earth but the
availability of solar resources relies on meteorological variables. In
this paper, forecasting models using the artificial neural network
are developed by the changing the input meteorological parameters
from five to seven. The two years data are used to train the model
whereas the testing is performed using one year data on different
seasons following single step ahead. The input parameters are
relative humidity, pressure, temperature, solar zenith angle, wind
speed, wind direction and precipitable water. Three artificial
neural network models (ANN-I5, ANN-I6, ANN-I7) are developed to
estimate the global horizontal irradiance and performance of all
developed models are measured on the basis of Mean Absolute
Percentage Error (MAPE), Relative Root Mean Square Error
(RRMSE) and Correlation Coefficient (R2
). Results indicates that
ANN-I7 shown better performance as comparison to other
developed models. The average MAPE and RRMSE of ANN models
such as ANN-I7, ANN-I6, ANN-I5 are 14.52%, 16.53%, 18.97% and
20.74%, 22.28%, 24.43% respectively. The ANN-I7 having an input
meteorological parameters relative humidity, pressure,
temperature, solar zenith angle, wind speed, wind direction and
perceptible water showed good accuracy as comparison to other
two developed models. This study indicates that accuracy of solar
irradiation forecasting depends on meteorological parameters