Daily Global Solar Radiation Modeling Using Multi-Layer Perceptron (MLP) Neural Networks
Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
[1] S. Rehman, M. Mohandes, Artificial neural network estimation of global
solar radiation using air temperature and relative humidity, Energy
Policy .63 (2008) 571-576.
[2] M.A. Behrang, E. Assareh, A.R. Noghrehabadi, and A. Ghanbarzadeh.
New sunshine-based models for predicting global solar radiation using
PSO (particle swarm optimization) technique. Energy 2011; 36: 3036-
3049. doi:10.1016/j.energy.2011.02.048.
[3] K. Bakirci, Correlations for estimation of daily global solar radiation
with hours of bright sunshine in Turkey, Energy (2009), doi:
10.1016/j.energy.2009.02.005.
[4] A. Angstrom, Solar and terrestrial radiation, Journal of the Royal
Meteorological Society.50 (1924) 121-126.
[5] V. Bahlel, H. Bakhsh, R. Srinivasan, A correlation for estimation of
global solar radiation, Energy. 12(2) (1987) 131-5.
[6] J. Almorox, C. Hontoria, Global solar estimation using sunshine
duration in Spain, Energy Conversion and Management. 11 (1967) 170-
2.
[7] B.G. Akinoglu, A. Ecevit, Construction of a quadratic model using
modified Angstrom coefficients to estimate global solar radiation, Solar
Energy. 45 (2) (1990) 85-92.
[8] S. Rehman, Solar radiation over Saudi Arabia and comparison with
empirical models, Energy .23 (12) (1998) 1077-1082.
[9] R. Aguiar, M. Collares-Pereira, A time dependent autoregressive,
Gaussian model for generating synthetic hourly radiation., Solar Energy.
49 (1992) 167-174.
[10] G. lewis, Estimates of irradiance over Zimbabwe, Solar Energy. 31
(1983) 609-612.
[11] R.K. Swartman, O. Ogunlade, Solar radiation estimates from common
parameters. Solar Energy 11 (1967) 170-172.
[12] Y.A.G Abdallah, New correlation of globar solar radiation with
meteorological parameters for Bahrain. Solar Energy 16 (1994) 111-120.
[13] J.I. Prieto, J.C.Martines-Garcia, D. Garcia, Correlation between global
solar irradiation and air temperature in Asturias, Spain, Sol. Energy
(2009), doi: 10.1016/j.solener.2009.01.012.
[14] A. Azadeh, A. Maghsoudi and S.Sohrabkhani, An integrated artificial
neural networks approach for predicting global radiation. Energy
Conversion and Management doi: 10.1016/j.enconman.2009.02.019.
[15] D. Elizondo, G. Hoogenboom and R. McClendon, Development of a
neural network to predict daily solar radiation, Agricultural and Forest
Meteorology.71 (1996) 115-132.
[16] S.M. Al-Alawi, H.A. Al-Hinai, An ANN-based approach for predicting
global solar radiation in locations with no measurements, Renewable
Energy. 14 (1-4) (1998) 199-20.
[17] I.T. Togrul, E. Onat, A study for estimating the solar radiation in Elaz─▒g╦ÿ
using geographical and meteorological data, Energy Conversion and
Management. 40 (1999) 1577-1584.
[18] A. Sozena, E. Arcaklioglub, M. Ozalpa, E.G. Kanitc, Use of artificial
neural networks for mapping of solar potential in Turkey, Applied
Energy. 77 (2004) 273-286.
[19] S.M. Robaa, Validation of existing models for estimating global solar
radiation over Egypt, Energy Conversion and Management. 50 (2009)
184-193.
[20] M.A. Behrang, E. Assareh, A. Ghanbarzadeh, A.R. Noghrehabadi. The
potential of different artificial neural network (ANN) techniques in daily
global solar radiation modeling based on meteorological data. Solar
Energy 2010; 84: 1468-1480.
[21] M.Mohandes, S.Rehman and T.O.Halawani, Estimation of global solar
radiation using artificial neural networks, Renewable Energy. 14 (1-4)
(1998) 179-184.
[22] M.Mohandes, A.Balghonaim, M.Kassas, S.Rehman, T.O.Halawani, Use
of radial basis functions for estimating monthly mean daily solar
radiation, Solar Energy. 68 (2) (2000) 161-168.
[23] L.Hontoria, J.Aguilera, J.Riesco, P.J. Zufiria, Recurrent neural
supervised models for generating solar radiation, Journal of Intelligent&
Robotic Systems. 31 (2001) 201-221.
[24] L.Hontoria, J. Aguilera, P.J. Zufiria, Generation of hourly irradiation
synthetic series using the neural network multilayer perceptron, Solar
Energy. 75 (2) (2002) 3441-446.
[25] I.Tasadduq, S.Rehman, K. Bubshait, Application of neural networks for
the prediction of hourly mean surface temperature in Saudi Arabia,
Renewable Energy. 25 (2002) 545-554.
[26] F.S.Tymvios, C.P. Jacovides ,S.C.Michaelides, C. Scouteli, Comparative
study of Angstrom-s and artificial neural networks- methodologies in
estimating global solar radiation, Solar Energy. 78 (2005) 752-762.
[27] J.L. Boscha, G. Lopez, F.J. Batllesa, Daily solar irradiation estimation
over a mountainous area using artificial neural networks, Renewable
Energy. 33 (2008) 1622-1628.
[28] J. Mubiru, E.J.K.B. Banda, Estimation of monthly average daily global
solar irradiation using artificial neural networks, Solar Energy. 82 (2008)
181-187.
[29] D.T. Pham, E. Koç, A. Ghanbarzadeh, S. Otri, Optimisation of the
Weights of Multi-Layered Perceptrons Using the Bees Algorithm. in:
Proceedings of 5th International Symposium on Intelligent
Manufacturing Systems, Sakarya University, Department of Industrial
Engineering, May 29-31, 2006, pp. 38-46
[30] A.S. Yilmaz, Z. Ozer, Pitch angle control in wind turbines above the
rated wind speed by multi-layer percepteron and Radial basis function
neural networks, Expert Systems with Applications. 36 (2009) 9767-
9775.
[31] D.T. Pham, X. Liu, Neural Networks for identification, prediction and
control, Springer verlag, london,1995.
[32] C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon
Press, Oxford, 1995.
[1] S. Rehman, M. Mohandes, Artificial neural network estimation of global
solar radiation using air temperature and relative humidity, Energy
Policy .63 (2008) 571-576.
[2] M.A. Behrang, E. Assareh, A.R. Noghrehabadi, and A. Ghanbarzadeh.
New sunshine-based models for predicting global solar radiation using
PSO (particle swarm optimization) technique. Energy 2011; 36: 3036-
3049. doi:10.1016/j.energy.2011.02.048.
[3] K. Bakirci, Correlations for estimation of daily global solar radiation
with hours of bright sunshine in Turkey, Energy (2009), doi:
10.1016/j.energy.2009.02.005.
[4] A. Angstrom, Solar and terrestrial radiation, Journal of the Royal
Meteorological Society.50 (1924) 121-126.
[5] V. Bahlel, H. Bakhsh, R. Srinivasan, A correlation for estimation of
global solar radiation, Energy. 12(2) (1987) 131-5.
[6] J. Almorox, C. Hontoria, Global solar estimation using sunshine
duration in Spain, Energy Conversion and Management. 11 (1967) 170-
2.
[7] B.G. Akinoglu, A. Ecevit, Construction of a quadratic model using
modified Angstrom coefficients to estimate global solar radiation, Solar
Energy. 45 (2) (1990) 85-92.
[8] S. Rehman, Solar radiation over Saudi Arabia and comparison with
empirical models, Energy .23 (12) (1998) 1077-1082.
[9] R. Aguiar, M. Collares-Pereira, A time dependent autoregressive,
Gaussian model for generating synthetic hourly radiation., Solar Energy.
49 (1992) 167-174.
[10] G. lewis, Estimates of irradiance over Zimbabwe, Solar Energy. 31
(1983) 609-612.
[11] R.K. Swartman, O. Ogunlade, Solar radiation estimates from common
parameters. Solar Energy 11 (1967) 170-172.
[12] Y.A.G Abdallah, New correlation of globar solar radiation with
meteorological parameters for Bahrain. Solar Energy 16 (1994) 111-120.
[13] J.I. Prieto, J.C.Martines-Garcia, D. Garcia, Correlation between global
solar irradiation and air temperature in Asturias, Spain, Sol. Energy
(2009), doi: 10.1016/j.solener.2009.01.012.
[14] A. Azadeh, A. Maghsoudi and S.Sohrabkhani, An integrated artificial
neural networks approach for predicting global radiation. Energy
Conversion and Management doi: 10.1016/j.enconman.2009.02.019.
[15] D. Elizondo, G. Hoogenboom and R. McClendon, Development of a
neural network to predict daily solar radiation, Agricultural and Forest
Meteorology.71 (1996) 115-132.
[16] S.M. Al-Alawi, H.A. Al-Hinai, An ANN-based approach for predicting
global solar radiation in locations with no measurements, Renewable
Energy. 14 (1-4) (1998) 199-20.
[17] I.T. Togrul, E. Onat, A study for estimating the solar radiation in Elaz─▒g╦ÿ
using geographical and meteorological data, Energy Conversion and
Management. 40 (1999) 1577-1584.
[18] A. Sozena, E. Arcaklioglub, M. Ozalpa, E.G. Kanitc, Use of artificial
neural networks for mapping of solar potential in Turkey, Applied
Energy. 77 (2004) 273-286.
[19] S.M. Robaa, Validation of existing models for estimating global solar
radiation over Egypt, Energy Conversion and Management. 50 (2009)
184-193.
[20] M.A. Behrang, E. Assareh, A. Ghanbarzadeh, A.R. Noghrehabadi. The
potential of different artificial neural network (ANN) techniques in daily
global solar radiation modeling based on meteorological data. Solar
Energy 2010; 84: 1468-1480.
[21] M.Mohandes, S.Rehman and T.O.Halawani, Estimation of global solar
radiation using artificial neural networks, Renewable Energy. 14 (1-4)
(1998) 179-184.
[22] M.Mohandes, A.Balghonaim, M.Kassas, S.Rehman, T.O.Halawani, Use
of radial basis functions for estimating monthly mean daily solar
radiation, Solar Energy. 68 (2) (2000) 161-168.
[23] L.Hontoria, J.Aguilera, J.Riesco, P.J. Zufiria, Recurrent neural
supervised models for generating solar radiation, Journal of Intelligent&
Robotic Systems. 31 (2001) 201-221.
[24] L.Hontoria, J. Aguilera, P.J. Zufiria, Generation of hourly irradiation
synthetic series using the neural network multilayer perceptron, Solar
Energy. 75 (2) (2002) 3441-446.
[25] I.Tasadduq, S.Rehman, K. Bubshait, Application of neural networks for
the prediction of hourly mean surface temperature in Saudi Arabia,
Renewable Energy. 25 (2002) 545-554.
[26] F.S.Tymvios, C.P. Jacovides ,S.C.Michaelides, C. Scouteli, Comparative
study of Angstrom-s and artificial neural networks- methodologies in
estimating global solar radiation, Solar Energy. 78 (2005) 752-762.
[27] J.L. Boscha, G. Lopez, F.J. Batllesa, Daily solar irradiation estimation
over a mountainous area using artificial neural networks, Renewable
Energy. 33 (2008) 1622-1628.
[28] J. Mubiru, E.J.K.B. Banda, Estimation of monthly average daily global
solar irradiation using artificial neural networks, Solar Energy. 82 (2008)
181-187.
[29] D.T. Pham, E. Koç, A. Ghanbarzadeh, S. Otri, Optimisation of the
Weights of Multi-Layered Perceptrons Using the Bees Algorithm. in:
Proceedings of 5th International Symposium on Intelligent
Manufacturing Systems, Sakarya University, Department of Industrial
Engineering, May 29-31, 2006, pp. 38-46
[30] A.S. Yilmaz, Z. Ozer, Pitch angle control in wind turbines above the
rated wind speed by multi-layer percepteron and Radial basis function
neural networks, Expert Systems with Applications. 36 (2009) 9767-
9775.
[31] D.T. Pham, X. Liu, Neural Networks for identification, prediction and
control, Springer verlag, london,1995.
[32] C.M. Bishop, Neural Networks for Pattern Recognition, Clarendon
Press, Oxford, 1995.
@article{"International Journal of Earth, Energy and Environmental Sciences:60374", author = "Seyed Fazel Ziaei Asl and Ali Karami and Gholamreza Ashari and Azam Behrang and Arezoo Assareh and N.Hedayat", title = "Daily Global Solar Radiation Modeling Using Multi-Layer Perceptron (MLP) Neural Networks", abstract = "Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
", keywords = "Multi-layer Perceptron (MLP) Neural Networks;Global Solar Radiation (GSR), Meteorological Parameters, Prediction.", volume = "5", number = "7", pages = "397-3", }
