Solar energy is one of the clean and healthy energies. Due to the high cost of required equipment to convert solar energy into the desired form, the economic facets must be addressed and the equipment should be installed in areas with higher accessible solar energy. However, due to the complex and time-consuming process of calculating solar radiation, it seems necessary to develop more simple models with higher estimation capability. Therefore, the present study investigated the prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM, in Sarpol-e-Zahab Township, Kermanshah, Iran. In this respect, the meteorological data of this township were collected. Then, the key parameters were selected by performing sensitivity analysis, and models were designed and optimized using ANFIS, ANN, and RSM methods. Moreover, respective correlation coefficients and mean square errors of each method were obtained (ANFIS (0.993 and 0.0005), ANN (0.996 and 0.00029), and RSM (0.996 and 0.00027), respectively). Also, the neural network and response surface methodology were superior to the ANFIS Model in terms of performance, simplicity, and speed. In short, the performance of the response surface methodology was slightly better than that of the neural network.
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Abdallah Y A G 1994 New correlation of global solar radiation with meteorological parameters for Bahrain; Sol. Energy 16 111–120.
Angstrom A 1924 Solar and terrestrial radiation; Quart. J. Roy. Meteorol. Soc. 50(210) 121–126.
Arkhipov M, Krueger E and Kurtener D 2008 Evaluation of ecological conditions using bioindicators: Application of fuzzy modeling; Lect. Notes Comput. Sci. 5072 491–500.
Azadeh A, Maghsoudi A and Sohrabkhani S 2009 An integrated artificial neural networks approach for predicting global radiation. Energ. Convers. Manag. 50 1497–1505.
Bayat K and Mirlatifi M 2009 Estimation of daily solar radiation using regression models and artificial neural network; J. Agr. Sci. Nat. Res. 16(3) 270–279.
Behrang M A, Assareh E, Ghanbarzadeh A and Noghrehabadi A 2010 The potential of different artificial neural network (ANN) techniques in daily global solar radiation modelling based on meteorological data; Sol. Energy 84 1468–1480.
Bojanowski J S, Donatelli M, Skidmore A K and Vrieling A 2013 An auto-calibration procedure for empirical solar radiation models; Environ. Model. Softw. 49 118–128.
Bristow K L and Campbell G S 1984 On the relationship between incoming solar radiation and daily maximum and minimum temperature; Agr. Forest Meteorol. 31 159–166.
Buragohain M and Mahanta C 2008 A novel approach for ANFIS modelling based on full factorial design; Appl. Softw. Comput. 8 609–625.
Chen R S, Ersi K, Yang J P, Lu S H and Zhao W Z 2004 Validation of five global radiation models with measured daily data in China; Energ. Convers. Manag. 45 1759–1769.
Cheng C B, Cheng C J and Lee E S 2002 Neuro-fuzzy and genetic algorithm in multiple response optimization; Comput. Math. Appl. 44 1503–1514.
Cooper P I 1969 The absorption of solar radiation in solar stills; Sol. Energy 12(3) 333–346.
Duffie J A and Beckman W A 1992 Solar Energy Engineering, Wiley, New York.
Fadare D, Irimisose I, Oni A and Falana A 2010 Modelling of solar energy potential in Africa using an artificial neural network; Am. J. Sci. India Res. 1(2) 144–157.
Majnoni-Heris A, Zand-Parsa S H, Sepaskhah A and Nazemosadat M J 2009 Development and evaluation of global solar radiation models based on sunshine hours and meteorological data; J. Water Soil Sci. 12 491–499.
Metin E H and Murat H 2008 Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system; Int. J. Refrig. 31 1426–1436.
Moghaddamnia A, Remesan R, Hassanpour Kashani M, Mohammadi M, Han D and Piri J 2009 Comparison of LLR, MLP, Elman, NNARX and ANFIS Models – with a case study in solar radiation estimation; J. Atmos. Sol.-Terr. Phys. 71(8–9) 975–982.
Muselli M, Notton G, Canaletti J L and Louche A 1998 Utilization of meteosat satellite-derived radiation data for integration of autonomous photovoltaic solar energy systems in remote areas; Energ. Convers. Manag. 39(2) 1–19.
Perez R, Seals R and Zelenka A 1997 Comparing satellite remote sensing and ground network measurements for the production of site/time specific irradiance data; Sol. Energy 60(2) 89–96.
Pons X and Ninyerola M 2008 Mapping a topographic global solar radiation model implemented in a GIS and refined with ground data; Int. J. Climatol. 28(13) 1821–1834.
Prescok J A 1940 Evaporation from a water surface in relation to solar radiation; Trans. Roy. Soc. S. Afr. 64 114–118.
Rehman S and Mohandes M 2008 Artificial neural network estimation of global solar radiation using air temperature and relative humidity; Energ. Policy 36 571–576.
Rehman Sh 1998 Solar radiation over Saudi Arabia and comparisons with empirical models; Energy 23(12) 1077–1082.
Sabziparvar A A and Shetaee H 2007 Estimation of global solar radiation in arid and semi-arid climates of East and West Iran; Energy 32 649–655.
Serge G 2001 Designing fuzzy inference systems from data: Interpretability oriented review; IEEE Trans. Fuzzy Syst. 9(3) 426–442.
Shamshirband S, Mohammadi K, Tong C W, Zamani M and Motamedi S S Ch 2016 A hybrid SVM-FFA method for prediction of monthly mean global solar radiation; Theor. Appl. Climatol. 125(1–2) 53–65.
Sozen A, Arcaklioglu E and Ozalp M 2004 Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data; Energ. Convers. Manag. 45 3033–3052.
Yaghoubi M A and Sabazevari A 1996 Further data on solar radiation in Shiraz, Iran; Renew. Energ. 7(4) 393–399.
The authors would like to acknowledge the financial support from Ministry of Science, Research and Technology, Tehran, Iran and the Vice Chancellor for Research and Technology of Razi University of Kermanshah.
Communicated by N V Chalapathi Rao
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Naderloo, L. Prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM (case study: Sarpol-e-Zahab Township, Iran). J Earth Syst Sci 129, 148 (2020). https://doi.org/10.1007/s12040-020-01414-z
- neural network
- solar radiation