IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v86y2009i4p554-564.html
   My bibliography  Save this article

Assessment of diffuse solar energy under general sky condition using artificial neural network

Author

Listed:
  • Alam, Shah
  • Kaushik, S.C.
  • Garg, S.N.

Abstract

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R2) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.

Suggested Citation

  • Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:4:p:554-564
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(08)00221-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2006. "Computation of beam solar radiation at normal incidence using artificial neural network," Renewable Energy, Elsevier, vol. 31(10), pages 1483-1491.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    3. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    4. Soares, Jacyra & Oliveira, Amauri P. & Boznar, Marija Zlata & Mlakar, Primoz & Escobedo, João F. & Machado, Antonio J., 2004. "Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique," Applied Energy, Elsevier, vol. 79(2), pages 201-214, October.
    5. Jacovides, C.P. & Tymvios, F.S. & Assimakopoulos, V.D. & Kaltsounides, N.A., 2006. "Comparative study of various correlations in estimating hourly diffuse fraction of global solar radiation," Renewable Energy, Elsevier, vol. 31(15), pages 2492-2504.
    6. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.
    2. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    3. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    4. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    5. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    6. Kuo, Chia-Wei & Chang, Wen-Chey & Chang, Keh-Chin, 2014. "Modeling the hourly solar diffuse fraction in Taiwan," Renewable Energy, Elsevier, vol. 66(C), pages 56-61.
    7. Oh, Myeongchan & Kim, Chang Ki & Kim, Boyoung & Yun, Changyeol & Kim, Jin-Young & Kang, Yongheack & Kim, Hyun-Goo, 2022. "Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms," Energy, Elsevier, vol. 241(C).
    8. Elminir, Hamdy K. & Azzam, Yosry A. & Younes, Farag I., 2007. "Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models," Energy, Elsevier, vol. 32(8), pages 1513-1523.
    9. Jamil, Basharat & Akhtar, Naiem, 2017. "Estimation of diffuse solar radiation in humid-subtropical climatic region of India: Comparison of diffuse fraction and diffusion coefficient models," Energy, Elsevier, vol. 131(C), pages 149-164.
    10. Huang, Kuo-Tsang, 2020. "Identifying a suitable hourly solar diffuse fraction model to generate the typical meteorological year for building energy simulation application," Renewable Energy, Elsevier, vol. 157(C), pages 1102-1115.
    11. Dahmani, Kahina & Notton, Gilles & Voyant, Cyril & Dizene, Rabah & Nivet, Marie Laure & Paoli, Christophe & Tamas, Wani, 2016. "Multilayer Perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements," Renewable Energy, Elsevier, vol. 90(C), pages 267-282.
    12. Marques Filho, Edson P. & Oliveira, Amauri P. & Vita, Willian A. & Mesquita, Francisco L.L. & Codato, Georgia & Escobedo, João F. & Cassol, Mariana & França, José Ricardo A., 2016. "Global, diffuse and direct solar radiation at the surface in the city of Rio de Janeiro: Observational characterization and empirical modeling," Renewable Energy, Elsevier, vol. 91(C), pages 64-74.
    13. Liu, Peirong & Tong, Xiaojuan & Zhang, Jinsong & Meng, Ping & Li, Jun & Zhang, Jingru, 2020. "Estimation of half-hourly diffuse solar radiation over a mixed plantation in north China," Renewable Energy, Elsevier, vol. 149(C), pages 1360-1369.
    14. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparative analysis of diffuse solar radiation models based on sky-clearness index and sunshine period for humid-subtropical climatic region of India: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 329-355.
    15. Karakoti, Indira & Pande, Bimal & Pandey, Kavita, 2011. "Evaluation of different diffuse radiation models for Indian stations and predicting the best fit model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2378-2384, June.
    16. Abreu, Edgar F.M. & Canhoto, Paulo & Costa, Maria João, 2019. "Prediction of diffuse horizontal irradiance using a new climate zone model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 28-42.
    17. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    18. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    19. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    20. Mondol, Jayanta Deb & Yohanis, Yigzaw G. & Norton, Brian, 2008. "Solar radiation modelling for the simulation of photovoltaic systems," Renewable Energy, Elsevier, vol. 33(5), pages 1109-1120.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:86:y:2009:i:4:p:554-564. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.