IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v166y2016icp117-127.html
   My bibliography  Save this article

Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation

Author

Listed:
  • Kocifaj, Miroslav
  • Kómar, Ladislav

Abstract

The optimum utilization of solar energy requires effective harvesting of both the direct and diffuse components of ground-reaching radiation. Although solar beams are typically key contributors to the total irradiance under cloudless conditions, the diffuse component becomes important especially in regions where clear skies are not dominant. Even if the cloud cover and cloud microphysics are known, it is not an easy task to estimate the diffuse irradiance at arbitrarily oriented sloped surfaces. This situation arises from the extreme difficulty in solving the radiative transfer equation in such a heterogeneous environment.

Suggested Citation

  • Kocifaj, Miroslav & Kómar, Ladislav, 2016. "Modeling diffuse irradiance under arbitrary and homogeneous skies: Comparison and validation," Applied Energy, Elsevier, vol. 166(C), pages 117-127.
    • Handle: RePEc:eee:appene:v:166:y:2016:i:c:p:117-127
    DOI: 10.1016/j.apenergy.2016.01.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916300046
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2016.01.024?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. Karakoti, Indira & Das, Prasun Kumar & Singh, S.K., 2012. "Predicting monthly mean daily diffuse radiation for India," Applied Energy, Elsevier, vol. 91(1), pages 412-425.
    2. Ranganathan, Raghuram & Mikhael, Wasfy & Kutkut, Nasser & Batarseh, Issa, 2011. "Adaptive sun tracking algorithm for incident energy maximization and efficiency improvement of PV panels," Renewable Energy, Elsevier, vol. 36(10), pages 2623-2626.
    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. Li, Danny H.W., 2010. "A review of daylight illuminance determinations and energy implications," Applied Energy, Elsevier, vol. 87(7), pages 2109-2118, July.
    5. Janjai, S. & Sricharoen, K. & Pattarapanitchai, S., 2011. "Semi-empirical models for the estimation of clear sky solar global and direct normal irradiances in the tropics," Applied Energy, Elsevier, vol. 88(12), pages 4749-4755.
    6. 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.
    7. Boland, John & Huang, Jing & Ridley, Barbara, 2013. "Decomposing global solar radiation into its direct and diffuse components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 749-756.
    8. Koussa, M. & Cheknane, A. & Hadji, S. & Haddadi, M. & Noureddine, S., 2011. "Measured and modelled improvement in solar energy yield from flat plate photovoltaic systems utilizing different tracking systems and under a range of environmental conditions," Applied Energy, Elsevier, vol. 88(5), pages 1756-1771, May.
    9. Li, Huashan & Ma, Weibin & Lian, Yongwang & Wang, Xianlong, 2010. "Estimating daily global solar radiation by day of year in China," Applied Energy, Elsevier, vol. 87(10), pages 3011-3017, October.
    10. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    11. 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.
    12. 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.
    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. Petržala, J. & Kómar, L. & Kocifaj, M., 2017. "An advanced clear-sky model for more accurate irradiance and illuminance predictions for arbitrarily oriented inclined surfaces," Renewable Energy, Elsevier, vol. 106(C), pages 212-221.
    2. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
    3. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
    4. Mettanant, Vichuda & Chaiwiwatworakul, Pipat & Chirarattananon, Surapong, 2017. "A model of Thai’s sky luminance distribution based on reduced CIE standard sky types," Renewable Energy, Elsevier, vol. 103(C), pages 739-749.
    5. Yao, Wanxiang & Song, Mengjia & Huang, Yu & Xu, Puyan & Li, Xianli & Su, Gang & Gao, Weijun, 2024. "A new anisotropic solar radiation model based on the principle of photothermal integration," Renewable Energy, Elsevier, vol. 226(C).
    6. Gueymard, Christian A. & Kocifaj, Miroslav, 2022. "Clear-sky spectral radiance modeling under variable aerosol conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Wang, Lunche & Lu, Yunbo & Zou, Ling & Feng, Lan & Wei, Jing & Qin, Wenmin & Niu, Zigeng, 2019. "Prediction of diffuse solar radiation based on multiple variables in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 151-216.
    8. Yao, Wanxiang & Zhang, Kang & Cao, Weixue & Li, Xianli & Wang, Yan & Wang, Xiao, 2022. "Research on the correlation between solar radiation and sky luminance based on the principle of photothermal integration," Renewable Energy, Elsevier, vol. 194(C), pages 1326-1342.
    9. Kafka, Jennifer & Miller, Mark A., 2020. "The dual angle solar harvest (DASH) method: An alternative method for organizing large solar panel arrays that optimizes incident solar energy in conjunction with land use," Renewable Energy, Elsevier, vol. 155(C), pages 531-546.
    10. Vamvakas, Ioannis & Salamalikis, Vasileios & Kazantzidis, Andreas, 2020. "Evaluation of enhancement events of global horizontal irradiance due to clouds at Patras, South-West Greece," Renewable Energy, Elsevier, vol. 151(C), pages 764-771.
    11. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    12. Lemos, Leonardo F.L. & Starke, Allan R. & Boland, John & Cardemil, José M. & Machado, Rubinei D. & Colle, Sergio, 2017. "Assessment of solar radiation components in Brazil using the BRL model," Renewable Energy, Elsevier, vol. 108(C), pages 569-580.

    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. Yao, Wanxiang & Zhang, Kang & Cao, Weixue & Li, Xianli & Wang, Yan & Wang, Xiao, 2022. "Research on the correlation between solar radiation and sky luminance based on the principle of photothermal integration," Renewable Energy, Elsevier, vol. 194(C), pages 1326-1342.
    2. 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.
    3. 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.
    4. 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.
    5. Das, Aparna & Paul, Saikat Kumar, 2015. "Artificial illumination during daytime in residential buildings: Factors, energy implications and future predictions," Applied Energy, Elsevier, vol. 158(C), pages 65-85.
    6. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    7. Wang, Lunche & Lu, Yunbo & Zou, Ling & Feng, Lan & Wei, Jing & Qin, Wenmin & Niu, Zigeng, 2019. "Prediction of diffuse solar radiation based on multiple variables in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 151-216.
    8. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
    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. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    11. Khorasanizadeh, Hossein & Mohammadi, Kasra, 2016. "Diffuse solar radiation on a horizontal surface: Reviewing and categorizing the empirical models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 338-362.
    12. Wang, Lunche & Gong, Wei & Li, Chen & Lin, Aiwen & Hu, Bo & Ma, Yingying, 2013. "Measurement and estimation of photosynthetically active radiation from 1961 to 2011 in Central China," Applied Energy, Elsevier, vol. 111(C), pages 1010-1017.
    13. Pan, Tao & Wu, Shaohong & Dai, Erfu & Liu, Yujie, 2013. "Estimating the daily global solar radiation spatial distribution from diurnal temperature ranges over the Tibetan Plateau in China," Applied Energy, Elsevier, vol. 107(C), pages 384-393.
    14. Sabzpooshani, Majid & Mohammadi, Kasra, 2014. "Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran," Energy, Elsevier, vol. 69(C), pages 571-577.
    15. Gulin, Marko & Vašak, Mario & Perić, Nedjeljko, 2013. "Dynamical optimal positioning of a photovoltaic panel in all weather conditions," Applied Energy, Elsevier, vol. 108(C), pages 429-438.
    16. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
    17. 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.
    18. Mohamed A. Ali & Ashraf Elsayed & Islam Elkabani & Mohammad Akrami & M. Elsayed Youssef & Gasser E. Hassan, 2024. "Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis," Energies, MDPI, vol. 17(17), pages 1-42, August.
    19. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    20. 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.

    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:166:y:2016:i:c:p:117-127. 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.