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Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model

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  • Janjai, Serm
  • Plaon, Piyanuch

Abstract

An artificial neural network (ANN) model for estimating sky luminance was developed. A 3-year period (2007-2009) of sky luminance data obtained from measurements at Nakhon Pathom (13.82°N, 100.04°E) and a 1-year period (2008) of the same type of data at Songkhla (7.20°N, 100.60°E), Thailand were used in this study. The ANN model was trained using a back propagation algorithm, based on 2 years data (2007-2008) at Nakhon Pathom for clear, partly cloudy and overcast skies. The trained ANN model was used to predict sky luminance at Nakhon Pathom for the year 2009 for the case of clear, partly cloudy and overcast skies. The results were compared with those of the CIE model. It was found that the ANN model performed better than CIE models for most cases. The ANN model trained with Nakhon Pathom data were also used to predict sky luminance at Songkhla and satisfactory results were obtained.

Suggested Citation

  • Janjai, Serm & Plaon, Piyanuch, 2011. "Estimation of sky luminance in the tropics using artificial neural networks: Modeling and performance comparison with the CIE model," Applied Energy, Elsevier, vol. 88(3), pages 840-847, March.
  • Handle: RePEc:eee:appene:v:88:y:2011:i:3:p:840-847
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    1. 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.
    2. Li, Danny H.W. & Tsang, Ernest K.W. & Cheung, K.L. & Tam, C.O., 2010. "An analysis of light-pipe system via full-scale measurements," Applied Energy, Elsevier, vol. 87(3), pages 799-805, March.
    3. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    4. Markou, M.T. & Kambezidis, H.D. & Bartzokas, A. & Katsoulis, B.D. & Muneer, T., 2005. "Sky type classification in Central England during winter," Energy, Elsevier, vol. 30(9), pages 1667-1674.
    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. Li, Danny H.W., 2010. "A review of daylight illuminance determinations and energy implications," Applied Energy, Elsevier, vol. 87(7), pages 2109-2118, July.
    7. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    8. Chel, Arvind & Tiwari, G.N. & Chandra, Avinash, 2009. "A model for estimation of daylight factor for skylight: An experimental validation using pyramid shape skylight over vault roof mud-house in New Delhi (India)," Applied Energy, Elsevier, vol. 86(11), pages 2507-2519, November.
    9. Chirarattananon, Surapong & Chaiwiwatworakul, Pipat, 2007. "Distributions of sky luminance and radiance of North Bangkok under standard distributions," Renewable Energy, Elsevier, vol. 32(8), pages 1328-1345.
    10. 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.
    11. Chel, Arvind & Tiwari, G.N. & Singh, H.N., 2010. "A modified model for estimation of daylight factor for skylight integrated with dome roof structure of mud-house in New Delhi (India)," Applied Energy, Elsevier, vol. 87(10), pages 3037-3050, October.
    12. Darula, Stanislav & Kittler, Richard & Kocifaj, Miroslav, 2010. "Luminous effectiveness of tubular light-guides in tropics," Applied Energy, Elsevier, vol. 87(11), pages 3460-3466, November.
    13. Bosch, J.L. & López, G. & Batlles, F.J., 2008. "Daily solar irradiation estimation over a mountainous area using artificial neural networks," Renewable Energy, Elsevier, vol. 33(7), pages 1622-1628.
    14. De Rosa, A. & Ferraro, V. & Kaliakatsos, D. & Marinelli, V., 2010. "Calculating indoor natural illuminance in overcast sky conditions," Applied Energy, Elsevier, vol. 87(3), pages 806-813, March.
    15. 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.
    16. 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.
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    2. Mangkuto, Rizki A. & Rohmah, Mardliyahtur & Asri, Anindya Dian, 2016. "Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics," Applied Energy, Elsevier, vol. 164(C), pages 211-219.
    3. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    4. Li, Danny H.W. & Chau, T.C. & Wan, Kevin K.W., 2014. "A review of the CIE general sky classification approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 563-574.
    5. Yao, Wanxiang & Song, Mengjia & Li, Xianli & Meng, Xi & Wang, Yan & Kong, Xiangru & Jiang, Jinming, 2024. "A new modified method of all-sky radiance distribution based on the principle of photothermal integration," Applied Energy, Elsevier, vol. 367(C).
    6. 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.

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