IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v70y2014icp374-381.html
   My bibliography  Save this article

Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model

Author

Listed:
  • Dahmani, Kahina
  • Dizene, Rabah
  • Notton, Gilles
  • Paoli, Christophe
  • Voyant, Cyril
  • Nivet, Marie Laure

Abstract

Converting measured horizontal global solar irradiance in tilted ones is a difficult task, particularly for a small time-step and for not-averaged data. Conventional methods (statistical, correlation, …) are not always efficient with time-step less than one hour; thus, we want to know if an ANN (Artificial Neural Network) is able to realize this conversion with a good accuracy when applied to 5-min solar radiation data of Bouzareah, Algeria. The ANN is developed and optimized using two years of solar data; the nRMSE (relative root means square error) is around 8% for the optimal configuration, which corresponds to a very good accuracy for such a short time-step.

Suggested Citation

  • Dahmani, Kahina & Dizene, Rabah & Notton, Gilles & Paoli, Christophe & Voyant, Cyril & Nivet, Marie Laure, 2014. "Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model," Energy, Elsevier, vol. 70(C), pages 374-381.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:374-381
    DOI: 10.1016/j.energy.2014.04.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214004241
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2014.04.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Cheng, C.L. & Chan, C.Y. & Chen, C.L., 2006. "An empirical approach to estimating monthly radiation on south-facing tilted planes for building application," Energy, Elsevier, vol. 31(14), pages 2940-2957.
    2. Mellit, A. & Benghanem, M. & Arab, A. Hadj & Guessoum, A., 2005. "An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria," Renewable Energy, Elsevier, vol. 30(10), pages 1501-1524.
    3. Olmo, F.J & Vida, J & Foyo, I & Castro-Diez, Y & Alados-Arboledas, L, 1999. "Prediction of global irradiance on inclined surfaces from horizontal global irradiance," Energy, Elsevier, vol. 24(8), pages 689-704.
    4. Robledo, Luis & soler, Alfonso, 1998. "Modelling irradiance on inclined planes with an anisotropic model," Energy, Elsevier, vol. 23(3), pages 193-201.
    5. 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.
    6. Muzathik, A.M. & Ibrahim, M.Z. & Samo, K.B. & Wan Nik, W.B., 2011. "Estimation of global solar irradiation on horizontal and inclined surfaces based on the horizontal measurements," Energy, Elsevier, vol. 36(2), pages 812-818.
    7. Notton, Gilles & Paoli, Christophe & Vasileva, Siyana & Nivet, Marie Laure & Canaletti, Jean-Louis & Cristofari, Christian, 2012. "Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks," Energy, Elsevier, vol. 39(1), pages 166-179.
    8. Wenxian, Lin & Wenfeng, Gao & Shaoxuan, Pu & Enrong, Lu, 1995. "Ratios of global radiation on a tilted to horizontal surface for Yunnan Province, China," Energy, Elsevier, vol. 20(8), pages 723-728.
    9. Mellit, A. & Kalogirou, S.A. & Shaari, S. & Salhi, H. & Hadj Arab, A., 2008. "Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system," Renewable Energy, Elsevier, vol. 33(7), pages 1570-1590.
    10. De Rosa, A. & Ferraro, V. & Kaliakatsos, D. & Marinelli, V., 2008. "Calculating diffuse illuminance on vertical surfaces in different sky conditions," Energy, Elsevier, vol. 33(11), pages 1703-1710.
    11. Li, Danny H.W. & Lau, Chris C.S. & Lam, Joseph C., 2005. "Predicting daylight illuminance on inclined surfaces using sky luminance data," Energy, Elsevier, vol. 30(9), pages 1649-1665.
    12. 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.
    13. Maafi, A. & Adane, A., 1998. "Analysis of the performances of the first-order two-state Markov model using solar radiation properties," Renewable Energy, Elsevier, vol. 13(2), pages 175-193.
    14. Youcef Ettoumi, F. & Mefti, A. & Adane, A. & Bouroubi, M.Y., 2002. "Statistical analysis of solar measurements in Algeria using beta distributions," Renewable Energy, Elsevier, vol. 26(1), pages 47-67.
    15. Pandey, Chanchal Kumar & Katiyar, A.K., 2009. "A note on diffuse solar radiation on a tilted surface," Energy, Elsevier, vol. 34(11), pages 1764-1769.
    16. Notton, Gilles & Paoli, Christophe & Ivanova, Liliana & Vasileva, Siyana & Nivet, Marie Laure, 2013. "Neural network approach to estimate 10-min solar global irradiation values on tilted planes," Renewable Energy, Elsevier, vol. 50(C), pages 576-584.
    17. Noorian, Ali Mohammad & Moradi, Isaac & Kamali, Gholam Ali, 2008. "Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces," Renewable Energy, Elsevier, vol. 33(6), pages 1406-1412.
    18. Jiang, Yingni, 2009. "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, Elsevier, vol. 34(9), pages 1276-1283.
    19. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    20. Ruiz, Enrique & Soler, Alfonso & Robledo, Luis, 2002. "Comparison of the Olmo model with global irradiance measurements on vertical surfaces at Madrid," Energy, Elsevier, vol. 27(10), pages 975-986.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luo, Yongqiang & Zhang, Ling & Wu, Jing & Wang, Xiliang & Liu, Zhongbing & Wu, Zhenghong, 2017. "Modeling of solar transmission through multilayer glazing facade using shading blinds with arbitrary geometrical and surface optical properties," Energy, Elsevier, vol. 128(C), pages 163-182.
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    4. Hsu-Yung Cheng & Chih-Chang Yu & Kuo-Chang Hsu & Chi-Chang Chan & Mei-Hui Tseng & Chih-Lung Lin, 2019. "Estimating Solar Irradiance on Tilted Surface with Arbitrary Orientations and Tilt Angles," Energies, MDPI, vol. 12(8), pages 1-14, April.
    5. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
    6. Heng, Shye Yunn & Asako, Yutaka & Suwa, Tohru & Nagasaka, Ken, 2019. "Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network," Renewable Energy, Elsevier, vol. 131(C), pages 168-179.
    7. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
    8. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    9. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    10. Luo, Yongqiang & Zhang, Ling & Liu, Zhongbing & Wu, Jing & Zhang, Yelin & Wu, Zhenghong & He, Xihua, 2017. "Performance analysis of a self-adaptive building integrated photovoltaic thermoelectric wall system in hot summer and cold winter zone of China," Energy, Elsevier, vol. 140(P1), pages 584-600.
    11. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    12. Đozić, Damir J. & Gvozdenac Urošević, Branka D., 2019. "Application of artificial neural networks for testing long-term energy policy targets," Energy, Elsevier, vol. 174(C), pages 488-496.
    13. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    14. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
    15. Bisoi, Ranjeeta & Dash, Deepak Ranjan & Dash, P.K. & Tripathy, Lokanath, 2022. "An efficient robust optimized functional link broad learning system for solar irradiance prediction," Applied Energy, Elsevier, vol. 319(C).
    16. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
    17. 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.

    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. Notton, Gilles & Paoli, Christophe & Vasileva, Siyana & Nivet, Marie Laure & Canaletti, Jean-Louis & Cristofari, Christian, 2012. "Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks," Energy, Elsevier, vol. 39(1), pages 166-179.
    2. Notton, Gilles & Paoli, Christophe & Ivanova, Liliana & Vasileva, Siyana & Nivet, Marie Laure, 2013. "Neural network approach to estimate 10-min solar global irradiation values on tilted planes," Renewable Energy, Elsevier, vol. 50(C), pages 576-584.
    3. 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.
    4. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    5. Khalil, Samy A. & Shaffie, A.M., 2016. "Evaluation of transposition models of solar irradiance over Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 105-119.
    6. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    7. Cheng, C.L. & Chan, C.Y. & Chen, C.L., 2005. "Empirical approach to BIPV evaluation of solar irradiation for building applications," Renewable Energy, Elsevier, vol. 30(7), pages 1055-1074.
    8. Escrig, H. & Batlles, F.J. & Alonso, J. & Baena, F.M. & Bosch, J.L. & Salbidegoitia, I.B. & Burgaleta, J.I., 2013. "Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast," Energy, Elsevier, vol. 55(C), pages 853-859.
    9. Cheng, C.L. & Chan, C.Y. & Chen, C.L., 2006. "An empirical approach to estimating monthly radiation on south-facing tilted planes for building application," Energy, Elsevier, vol. 31(14), pages 2940-2957.
    10. Ayvazoğluyüksel, Özge & Filik, Ümmühan Başaran, 2018. "Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 639-653.
    11. Khalil, Samy A. & Shaffie, A.M., 2013. "A comparative study of total, direct and diffuse solar irradiance by using different models on horizontal and inclined surfaces for Cairo, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 853-863.
    12. Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
    13. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2011. "Optimization of an artificial neural network dedicated to the multivariate forecasting of daily global radiation," Energy, Elsevier, vol. 36(1), pages 348-359.
    14. 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.
    15. Bayrakçı, Hilmi Cenk & Demircan, Cihan & Keçebaş, Ali, 2018. "The development of empirical models for estimating global solar radiation on horizontal surface: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2771-2782.
    16. García, Ignacio & de Blas, Marian & Hernández, Begoña & Sáenz, Carlos & Torres, José Luis, 2021. "Diffuse irradiance on tilted planes in urban environments: Evaluation of models modified with sky and circumsolar view factors," Renewable Energy, Elsevier, vol. 180(C), pages 1194-1209.
    17. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    18. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    19. Hafez, A.Z. & Soliman, A. & El-Metwally, K.A. & Ismail, I.M., 2017. "Tilt and azimuth angles in solar energy applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 147-168.
    20. Shamshirband, Shahaboddin & Mohammadi, Kasra & Khorasanizadeh, Hossein & Yee, Por Lip & Lee, Malrey & Petković, Dalibor & Zalnezhad, Erfan, 2016. "Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 428-435.

    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:energy:v:70:y:2014:i:c:p:374-381. 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.journals.elsevier.com/energy .

    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.