A Simplified Model for Generating Sequences of Global Solar Radiation
Data for Isolated Sites: Using Artificial Neural Network and a Library
of Markov Transition Matrices Approach
The purpose of this work is to develop a hybrid model which will
be used to predict the daily global solar radiation data by combining
between an artificial neural network (ANN) and a library of Markov
transition matrices (MTM) approach. Developed model can generate
a sequence of global solar radiation data using a minimum of input
data (latitude, longitude and altitude), especially in isolated sites.
A data base of daily global solar radiation data has been collected
from 60 meteorological stations in Algeria during 1991â2000. Also
a typical meteorological year (TMY) has been built from this database.
Firstly, a neural network block has been trained based on 60 known
monthly solar radiation data from the TMY. In this way, the network
was trained to accept and even handle a number of unusual cases.
The neural network can generate the monthly solar radiation data.
Secondly, these data have been divided by corresponding extraterrestrial
value in order to obtain the monthly clearness index values. Based
on these monthly clearness indexes and using a library of MTM block
we can generate the sequences of daily clearness indexes. Known data
were subsequently used to investigate the accuracy of the prediction.
Furthermore, the unknown validation data set produced very accurate
prediction; with an RMSE error not exceeding 8% between the measured
and predicted data. A correlation coefficient ranging from 90% and
92% have been obtained; also this model has been compared to the
traditional models AR, ARMA, Markov chain, MTM and measured data.
Results obtained indicate that the proposed model can successfully
be used for the estimation of the daily solar radiation data for
any locations in Algeria by using as input the altitude, the longitude,
and the latitude. Also, the model can be generalized for any location
in the world. An application of sizing PV systems in isolated sites
has been applied in order to confirm the validity of this model.
%0 Journal Article
%1 Mellit.Benghanem.ea2005
%A Mellit, A.
%A Benghanem, M.
%A Arab, A. Hadj
%A Guessoum, A.
%D 2005
%J Solar Energy
%K Artificial Global Hybrid Markov Prediction clearness data, index, matrices, model, network, neural radiation solar transition
%T A Simplified Model for Generating Sequences of Global Solar Radiation
Data for Isolated Sites: Using Artificial Neural Network and a Library
of Markov Transition Matrices Approach
%X The purpose of this work is to develop a hybrid model which will
be used to predict the daily global solar radiation data by combining
between an artificial neural network (ANN) and a library of Markov
transition matrices (MTM) approach. Developed model can generate
a sequence of global solar radiation data using a minimum of input
data (latitude, longitude and altitude), especially in isolated sites.
A data base of daily global solar radiation data has been collected
from 60 meteorological stations in Algeria during 1991â2000. Also
a typical meteorological year (TMY) has been built from this database.
Firstly, a neural network block has been trained based on 60 known
monthly solar radiation data from the TMY. In this way, the network
was trained to accept and even handle a number of unusual cases.
The neural network can generate the monthly solar radiation data.
Secondly, these data have been divided by corresponding extraterrestrial
value in order to obtain the monthly clearness index values. Based
on these monthly clearness indexes and using a library of MTM block
we can generate the sequences of daily clearness indexes. Known data
were subsequently used to investigate the accuracy of the prediction.
Furthermore, the unknown validation data set produced very accurate
prediction; with an RMSE error not exceeding 8% between the measured
and predicted data. A correlation coefficient ranging from 90% and
92% have been obtained; also this model has been compared to the
traditional models AR, ARMA, Markov chain, MTM and measured data.
Results obtained indicate that the proposed model can successfully
be used for the estimation of the daily solar radiation data for
any locations in Algeria by using as input the altitude, the longitude,
and the latitude. Also, the model can be generalized for any location
in the world. An application of sizing PV systems in isolated sites
has been applied in order to confirm the validity of this model.
@article{Mellit.Benghanem.ea2005,
abstract = {The purpose of this work is to develop a hybrid model which will
be used to predict the daily global solar radiation data by combining
between an artificial neural network (ANN) and a library of Markov
transition matrices (MTM) approach. Developed model can generate
a sequence of global solar radiation data using a minimum of input
data (latitude, longitude and altitude), especially in isolated sites.
A data base of daily global solar radiation data has been collected
from 60 meteorological stations in Algeria during 1991â2000. Also
a typical meteorological year (TMY) has been built from this database.
Firstly, a neural network block has been trained based on 60 known
monthly solar radiation data from the TMY. In this way, the network
was trained to accept and even handle a number of unusual cases.
The neural network can generate the monthly solar radiation data.
Secondly, these data have been divided by corresponding extraterrestrial
value in order to obtain the monthly clearness index values. Based
on these monthly clearness indexes and using a library of MTM block
we can generate the sequences of daily clearness indexes. Known data
were subsequently used to investigate the accuracy of the prediction.
Furthermore, the unknown validation data set produced very accurate
prediction; with an RMSE error not exceeding 8% between the measured
and predicted data. A correlation coefficient ranging from 90% and
92% have been obtained; also this model has been compared to the
traditional models AR, ARMA, Markov chain, MTM and measured data.
Results obtained indicate that the proposed model can successfully
be used for the estimation of the daily solar radiation data for
any locations in Algeria by using as input the altitude, the longitude,
and the latitude. Also, the model can be generalized for any location
in the world. An application of sizing PV systems in isolated sites
has been applied in order to confirm the validity of this model.},
added-at = {2011-09-01T13:26:03.000+0200},
author = {Mellit, A. and Benghanem, M. and Arab, A. Hadj and Guessoum, A.},
biburl = {https://www.bibsonomy.org/bibtex/2d3e77165b5d76e471282cb7381bec06f/procomun},
file = {Mellit.Benghanem.ea2005.pdf:Mellit.Benghanem.ea2005.pdf:PDF},
interhash = {5bbd171117f7be6d60fa82e10c2bc2df},
intrahash = {d3e77165b5d76e471282cb7381bec06f},
journal = {Solar Energy},
keywords = {Artificial Global Hybrid Markov Prediction clearness data, index, matrices, model, network, neural radiation solar transition},
owner = {oscar},
refid = {Mellit.Benghanem.ea2005},
timestamp = {2011-09-02T08:25:25.000+0200},
title = {A Simplified Model for Generating Sequences of Global Solar Radiation
Data for Isolated Sites: Using Artificial Neural Network and a Library
of Markov Transition Matrices Approach},
year = 2005
}