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Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks

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  • Notton, Gilles
  • Paoli, Christophe
  • Vasileva, Siyana
  • Nivet, Marie Laure
  • Canaletti, Jean-Louis
  • Cristofari, Christian

Abstract

Calculating global solar irradiation from global horizontal irradiation only is a difficult task, especially when the time step is small and the data are not averaged. We used an Artificial Neural Network (ANN) to realize this conversion. The ANN is optimized and tested on the basis of five years of solar data; the accuracy of the optimal configuration is around 6% for the RRMSE (relative root mean square error) and around 3.5% for the RMAE (relative mean absolute value) i.e. a better performance than the empirical correlations available in the literature.

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  • 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.
    • Handle: RePEc:eee:energy:v:39:y:2012:i:1:p:166-179
    DOI: 10.1016/j.energy.2012.01.038
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    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. 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.
    3. Robledo, Luis & soler, Alfonso, 1998. "Modelling irradiance on inclined planes with an anisotropic model," Energy, Elsevier, vol. 23(3), pages 193-201.
    4. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    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. Gopinathan, K.K. & Soler, Alfonso, 1995. "Diffuse radiation models and monthly-average, daily, diffuse data for a wide latitude range," Energy, Elsevier, vol. 20(7), pages 657-667.
    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. 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.
    10. 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.
    11. Jiang, Yingni, 2008. "Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models," Energy Policy, Elsevier, vol. 36(10), pages 3833-3837, October.
    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. 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.
    14. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
    15. 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.
    16. 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.
    17. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    18. Mohandes, M. & Rehman, S. & Halawani, T.O., 1998. "Estimation of global solar radiation using artificial neural networks," Renewable Energy, Elsevier, vol. 14(1), pages 179-184.
    Full references (including those not matched with items on IDEAS)

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    6. 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.
    7. Ludmil Stoyanov & Ivan Bachev & Zahari Zarkov & Vladimir Lazarov & Gilles Notton, 2021. "Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes," Energies, MDPI, vol. 14(11), pages 1-28, May.
    8. Shaddel, Mehdi & Javan, Dawood Seyed & Baghernia, Parisa, 2016. "Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using Artificial Neural Network for case study of Mashhad," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 59-67.
    9. 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.
    10. Li, Danny H.W. & Lou, Siwei & Lam, Joseph C. & Wu, Ronald H.T., 2016. "Determining solar irradiance on inclined planes from classified CIE (International Commission on Illumination) standard skies," Energy, Elsevier, vol. 101(C), pages 462-470.
    11. Maria. C. Bueso & José Miguel Paredes-Parra & Antonio Mateo-Aroca & Angel Molina-García, 2020. "A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    12. Li, Honglian & He, Xi & Hu, Yao & Lv, Wen & Yang, Liu, 2024. "Research on the generation method of missing hourly solar radiation data based on multiple neural network algorithm," Energy, Elsevier, vol. 287(C).
    13. Mohammadi, Kasra & Shamshirband, Shahaboddin & Petković, Dalibor & Khorasanizadeh, Hossein, 2016. "Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1570-1579.
    14. 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.
    15. Jiaojiao Feng & Weizhen Wang & Jing Li, 2018. "An LM-BP Neural Network Approach to Estimate Monthly-Mean Daily Global Solar Radiation Using MODIS Atmospheric Products," Energies, MDPI, vol. 11(12), pages 1-14, December.
    16. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    17. 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.
    18. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    19. Lukač, Niko & Seme, Sebastijan & Žlaus, Danijel & Štumberger, Gorazd & Žalik, Borut, 2014. "Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data," Energy, Elsevier, vol. 66(C), pages 598-609.
    20. Li, Danny H.W. & Chau, Natalie T.C. & Wan, Kevin K.W., 2013. "Predicting daylight illuminance and solar irradiance on vertical surfaces based on classified standard skies," Energy, Elsevier, vol. 53(C), pages 252-258.

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