IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v53y2013icp86-93.html
   My bibliography  Save this article

Comparative building simulation study utilising measured and estimated solar irradiance for Australian locations

Author

Listed:
  • Copper, J.K.
  • Sproul, A.B.

Abstract

The focus of this paper is to investigate the impact of using estimated solar irradiance data in building energy simulations. A series of annual weather files were developed with the estimates from two global and three diffuse/direct irradiance models for three locations in Australia. A weather file was also developed using the modelled values of global, diffuse and direct irradiance as estimated by EnergyPlus' (Version 7.0.0) auxiliary weather generator program. In addition weather files were generated with measured irradiance data and satellite derived irradiance data from the Australian Bureau of Meteorology. The influence of irradiance data was tested via the simulation of a simple 15 × 15 × 3.5 m building under two construction scenarios with varying window to wall ratios (WWR). This study indicates that the level of bias and uncertainty in the simulation results was low when global irradiance was measured and only diffuse and direct irradiance were estimated. However, when global irradiance was unknown the level of bias and uncertainty increased significantly and was shown to be highly dependent on the WWR, whilst only the level of bias was shown to be dependent on the building envelope construction. The results also indicated that the Skartveit diffuse/direct irradiance model used in combination with the BOM's satellite derived global irradiance data set achieved better results than the complete (global and direct) satellite derived irradiance data set from the BOM. The worst correlation in the simulation results occurred for the case where the irradiance data was estimated by EnergyPlus' auxiliary weather generator program.

Suggested Citation

  • Copper, J.K. & Sproul, A.B., 2013. "Comparative building simulation study utilising measured and estimated solar irradiance for Australian locations," Renewable Energy, Elsevier, vol. 53(C), pages 86-93.
    • Handle: RePEc:eee:renene:v:53:y:2013:i:c:p:86-93
    DOI: 10.1016/j.renene.2012.10.054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148112007057
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2012.10.054?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. Copper, J.K. & Sproul, A.B., 2012. "Comparative study of mathematical models in estimating solar irradiance for Australia," Renewable Energy, Elsevier, vol. 43(C), pages 130-139.
    2. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
    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. Pérez-Burgos, Ana & Román, Roberto & Bilbao, Julia & de Miguel, Argimiro & Oteiza, Pilar, 2015. "Reconstruction of long-term direct solar irradiance data series using a model based on the Cloud Modification Factor," Renewable Energy, Elsevier, vol. 77(C), pages 115-124.
    2. Copper, J.K. & Sproul, A.B. & Jarnason, S., 2016. "Photovoltaic (PV) performance modelling in the absence of onsite measured plane of array irradiance (POA) and module temperature," Renewable Energy, Elsevier, vol. 86(C), pages 760-769.
    3. Lin, Chun-Tin & Chang, Keh-Chin & Chung, Kung-Ming, 2023. "Re-modeling the solar diffuse fraction in Taiwan on basis of a typical-meteorological-year data," Renewable Energy, Elsevier, vol. 204(C), pages 823-835.
    4. Every, Jeremy P. & Li, Li & Dorrell, David G., 2020. "Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations," Renewable Energy, Elsevier, vol. 147(P1), pages 2453-2469.
    5. Bertrand, Cédric & Vanderveken, Gilles & Journée, Michel, 2015. "Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium," Renewable Energy, Elsevier, vol. 74(C), pages 618-626.
    6. Schinke-Nendza, A. & von Loeper, F. & Osinski, P. & Schaumann, P. & Schmidt, V. & Weber, C., 2021. "Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies," Applied Energy, Elsevier, vol. 304(C).
    7. Abhnil Amtesh Prasad & Merlinde Kay, 2020. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar," Energies, MDPI, vol. 13(2), pages 1-22, January.
    8. Jentsch, Mark F. & James, Patrick A.B. & Bourikas, Leonidas & Bahaj, AbuBakr S., 2013. "Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates," Renewable Energy, Elsevier, vol. 55(C), pages 514-524.
    9. Mayer, Martin János & Yang, Dazhi, 2023. "Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    10. Deo, Ravinesh C. & Şahin, Mehmet, 2017. "Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 828-848.
    11. 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.
    12. Copper, J.K. & Sproul, A.B., 2012. "Comparative study of mathematical models in estimating solar irradiance for Australia," Renewable Energy, Elsevier, vol. 43(C), pages 130-139.
    13. Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
    14. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    15. 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.
    16. Lauret, Philippe & Boland, John & Ridley, Barbara, 2013. "Bayesian statistical analysis applied to solar radiation modelling," Renewable Energy, Elsevier, vol. 49(C), pages 124-127.
    17. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    18. David, Mathieu & Lauret, Philippe & Boland, John, 2013. "Evaluating tilted plane models for solar radiation using comprehensive testing procedures, at a southern hemisphere location," Renewable Energy, Elsevier, vol. 51(C), pages 124-131.
    19. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    20. Haghdadi, Navid & Copper, Jessie & Bruce, Anna & MacGill, Iain, 2017. "A method to estimate the location and orientation of distributed photovoltaic systems from their generation output data," Renewable Energy, Elsevier, vol. 108(C), pages 390-400.

    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:renene:v:53:y:2013:i:c:p:86-93. 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/renewable-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.