Estimating solar irradiance using genetic programming technique and meteorological records

Rami Al-Hajj; Ali Assi; Rami Al-Hajj; Ali Assi

doi:10.3934/energy.2017.5.798

AIMS Energy

2017, Volume 5, Issue 5: 798-813. doi: 10.3934/energy.2017.5.798

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Research article

Estimating solar irradiance using genetic programming technique and meteorological records

Rami Al-Hajj ^,,
Ali Assi

1.
Department of Math and Statistics for Engineering, American University of the Middle East, Egaila, Kuwait
2.
Department of Electrical and Electronics Engineering, International University of Beirut, Lebanon

Received: 29 April 2017 Accepted: 01 August 2017 Published: 15 August 2017

Solar irradiance is one of the most important parameters that need to be estimated and modeled before engaging in any solar energy project. This article describes a non-linear regression model based on genetic programming technique for estimating solar irradiance in a specific region in the United Arab Emirates. The genetic programming is an evolutionary computing technique that enables automatic search for complex solutions. The best nonlinear modeling function that can estimate the global solar radiation on horizontal will be developed taking into account measured meteorological data. A reference approach to model the solar radiation is first presented. An enhanced approach is then presented which consists of multi nonlinear functions of regression in a parallel structure where each function is designed to estimate the global solar irradiance in a specific seasonal period of the year. Statistical analysis measures have been used to evaluate the performance of the proposed approaches. The obtained results are comparable with the outcomes of models developed by other researchers in the field.
- global solar radiation,
- estimation,
- genetic programming,
- evolutionary computation,
- meteorological data,
- statistical analysis,
- RMSE,
- MBE
Citation: Rami Al-Hajj, Ali Assi. Estimating solar irradiance using genetic programming technique and meteorological records[J]. AIMS Energy, 2017, 5(5): 798-813. doi: 10.3934/energy.2017.5.798

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Abstract

Solar irradiance is one of the most important parameters that need to be estimated and modeled before engaging in any solar energy project. This article describes a non-linear regression model based on genetic programming technique for estimating solar irradiance in a specific region in the United Arab Emirates. The genetic programming is an evolutionary computing technique that enables automatic search for complex solutions. The best nonlinear modeling function that can estimate the global solar radiation on horizontal will be developed taking into account measured meteorological data. A reference approach to model the solar radiation is first presented. An enhanced approach is then presented which consists of multi nonlinear functions of regression in a parallel structure where each function is designed to estimate the global solar irradiance in a specific seasonal period of the year. Statistical analysis measures have been used to evaluate the performance of the proposed approaches. The obtained results are comparable with the outcomes of models developed by other researchers in the field.

References

[1]	Kassem AS, Aboukarima AM, EL Ashmawy NM (2009) Development of Neural Network Model to Estimate Hourly Total and Diffuse Solar Radiation on Horizontal Surface at Alexandria City (Egypt). J Appl Sci Res 5: 2006-2015.
[2]	Assi A, Jama M (2010) Estimating Global Solar Radiation on Horizontal from Sunshine Hours in Abu Dhabi –UAE. Advances in Energy Planning, Environmental Education and Renewable Energy Sources, 4th WSEAS international Conference on Renewable Energy Sources, 101-108.
[3]	Podestá G, Núñez L, Villanueva C, et al. (2004) Estimating daily solar radiation in the Argentine Pampas. Agr Forest Meteorol 123: 41-53. doi: 10.1016/j.agrformet.2003.11.002
[4]	Almorox J, Benito M, Hontoria C (2008) Estimation of global solar radiation in Venezuela. Interciencia 33: 280-283.
[5]	Falayi E, Adepitan J, Rabiu A (2008) Empirical models for the correlation of global solar radiation with meteorological data for Iseyin, Nigeria. Int J Phys Sci 3: 210-216.
[6]	Fortin J, Anctil F, Parent L, et al. (2008) Comparison of empirical daily surface incoming solar radiation models. Agr Forest Meteorol 148: 1332-1340. doi: 10.1016/j.agrformet.2008.03.012
[7]	Togrul I, Togrul H (2002) Global solar radiation over Turkey: Comparison of predicted and measured data. Renew Energy 25: 55-67. doi: 10.1016/S0960-1481(00)00197-X
[8]	Walthall C, Dulaney W, Anderson M, et al. (2004) A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery. Remote Sens Environ 92: 465-474. doi: 10.1016/j.rse.2004.06.003
[9]	Bakirci K (2009) Correlation for estimation of daily global solar radiation with hours of bright sunshine in Turkey. Energy 34: 485-501. doi: 10.1016/j.energy.2009.02.005
[10]	Tadros M (2000) Uses of sunshine duration to estimate the global solar radiation over eight meteorological stations in Egypt. Renew Energy 21: 231-246. doi: 10.1016/S0960-1481(00)00009-4
[11]	Al-Lawati A, Dorvlo A, Jervase J (2003) Monthly average daily solar radiation and clearness index contour maps over Oman. Energ Convers Manage 44: 691-670. doi: 10.1016/S0196-8904(02)00080-8
[12]	Zhou J, Yezheng Wu, Gang Y (2005) General formula for estimation of monthly average daily global solar radiation in China. Energ Convers Manage 46: 257-268. doi: 10.1016/j.enconman.2004.02.020
[13]	Ball R, Purcell C, Carey S (2004) Evaluation of Solar Radiation Prediction Models in North America. Agron J 96: 391-397. doi: 10.2134/agronj2004.3910
[14]	Menges H, Ertekin C, Sonmete M (2006) Evaluation of solar radiation models for Konya, Turkey. Energ Convers Manage 47: 3149-3173 doi: 10.1016/j.enconman.2006.02.015
[15]	Şahin A (2007) A new formulation for solar irradiation and sunshine duration estimation. Int J Energy Res 31: 109-118. doi: 10.1002/er.1229
[16]	Ulgen K, Hepbasli A (2002) Comparison of solar radiation correlations for Izmir, Turkey. Int J Energy Res 26: 413-430. doi: 10.1002/er.794
[17]	Angström A (1924) Solar and terrestrial radiation. Quart J Roy Met Soc 50: 121-125.
[18]	Boccol M, Willington E, Arias M (2010) Comparison of Regression and Neural Networks Models to estimate Solar Radiation. Chilean J Agr Res 70: 428-435.
[19]	Mohandes M, Rehman S, Halawani T (1998) Estimation of Global Solar Radiation Using Artificial Neural Networks. Renew Energy 14: 179-184. doi: 10.1016/S0960-1481(98)00065-2
[20]	Mohandes M, Balghonaim A, Kassas M, et al. (2000) Use of Radial Basis Functions for Estimating Monthly Mean Daily Solar Radiation. Solar Energy 68: 161-168. doi: 10.1016/S0038-092X(99)00071-7
[21]	Rehman S, Mohandes M (2008) Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36: 571-576. doi: 10.1016/j.enpol.2007.09.033
[22]	Tasadduq I, Rehman S, Bubshait K (2002) Application of neural networks for the prediction of hourly mean surface temperature in Saudi Arabia. Renew Energy 25: 545-554. doi: 10.1016/S0960-1481(01)00082-9
[23]	Krishnaiah T, Srinivasa RS, Madhumurthy K, et al. (2007) A Neural Network Approach for Modelling Global Solar Radiation. Appl Sci Res 3: 1105-1111.
[24]	Elminir H, Areed F, Elsayed T (2005) Estimation of solar radiation components incident on Helwan site using neural networks. Solar Energy 79: 270-279. doi: 10.1016/j.solener.2004.11.006
[25]	Assi A, Al-Shamisi M, Jama M (2010) Prediction of Monthly Average Daily Global Solar Radiation in Al Ain City–UAE Using Artificial Neural Networks, Proceedings of the 4th International Conference on Renewable Energy Sources , Tunisia, 109-113.
[26]	Assi A, Al-Shamisi M (2010) Prediction of Monthly Average Daily Global Solar Radiation in Al Ain City–UAE Using Artificial Neural Networks. Proceedings of the 25th European Photovoltaic Solar Energy Conference, Spain, 508-512.
[27]	Antonanzas-Torres F, Sanz-Garcia A, Martınez-de-Pison F, et al. (2013) Evaluation and improvement of empirical models of global solar irradiation: Case study northern Spain. Renew Energy 60: 604-614. doi: 10.1016/j.renene.2013.06.008
[28]	Ahmed A, Adam M (2013) Estimate of Global Solar Radiation by using Artificial Neural Nework in Qena, Upper Egypt. J Clean Energy Technol 1(2): 148-150.
[29]	Khatib T, Mohamed A, Mahmoud M, et al. (2012) Estimating Global Solar Energy Using Multilayer Perception Artificial Neural Network. Int J Energy 6(1): 82-87.
[30]	Ramedani Z, Omid M, Keyhani A, et al. (2014) Potential of radial basis function based support vector regression for global solar radiation prediction. Renew Sust Energy Rev 39:1005-1011. doi: 10.1016/j.rser.2014.07.108
[31]	Olatomiwa L, Mekhilefa S, Shamshirband S, et al. (2015) A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy 115: 632-644. doi: 10.1016/j.solener.2015.03.015
[32]	Mohammadi K, Shamshirband S, Danesh AS, et al. (2016) Temperature-based estimation of global solar radiation using soft computing methodologies. Theor Appl Climatol 125: 101-112 doi: 10.1007/s00704-015-1487-x
[33]	Kisi O (2014) Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach. Energy 64: 429-436 doi: 10.1016/j.energy.2013.10.009
[34]	Mohammadi K, Shamshirband S, Kamsin A, et al. (2016) Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure. Renew Sust Energy Rev 63: 423-434. doi: 10.1016/j.rser.2016.05.065
[35]	Demirhan H, Atilgan Y (2015) New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique. Energy Convers Manage 106: 1013-1023. doi: 10.1016/j.enconman.2015.10.038
[36]	Pan I, Pandey DS, Das S (2013) Global solar irradiation prediction using a multi-gene genetic programming approach. J Renew Sust Energy 5: 063129. doi: 10.1063/1.4850495
[37]	Baser F, Demirhan H (2017) A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation. Energy 123: 229-240. doi: 10.1016/j.energy.2017.02.008
[38]	Schwaerzel R, Bylander T (2006) Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics. Genetic and Evolutionary Computation Conference Gecco'06 1: 955-956.
[39]	Agapitos A, Dyson M, Kovalchuk J, et al. (2008) On the genetic programming of time-series predictors for supply chain management, proceedings of the 10th annual conference on genetic and evolutionary computation, Atlanta 1: 1163-1170.
[40]	Srinivas M, Patnail L (1994) Genetic algorithms, A survey. IEEE Computer 27(6): 17-26.
[41]	Poli R, Langdon W, McPhee N, et al. (2007) Genetic programming, An introductory tutorial and a survey of techniques and applications. University of Essex, UK, Tech Rep CES-475. Available from: http://cswww.sx.ac.uk/staff/rpoli/technical-reports/tr-ces-475.pdf
[42]	Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Available from: http://www.gp-field-guide.org.uk/
[43]	Riolo R, Vladislavleva E, Moore J (2011) Genetic programming theory and practice IX. Springer Science & Business Media, ISBN 1461417708.
[44]	Silva S, Almeida J (2003) A Genetic programming toolbox for MATLAB, Version 3. ECOS Evolutionary and complex Systems Group, University of Coimbra-portugal.
[45]	Silva S, Costa E (2005) Resource-Limited genetic programming: The dynamic approach. Proceedings of GECCO-2005, 1673-1680.
[46]	Silva S, Costa E (2004) Dynamic limits for bloat control - Variations on Size and Depth. Proceedings of GECCO'04, 666-677.
[47]	Silva S, Almeida J (2003) Dynamic maximum tree depth - A Simple technique for avoiding bloat in tree-based GP. Proceedings of GECCO-2003, 1776-1787.

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