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Interpolating and Estimating Horizontal Diffuse Solar Irradiation to Provide UK-Wide Coverage: Selection of the Best Performing Models

Author

Listed:
  • Diane Palmer

    (Centre for Renewable Energy Systems Technology, School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK)

  • Ian Cole

    (Centre for Renewable Energy Systems Technology, School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK)

  • Tom Betts

    (Centre for Renewable Energy Systems Technology, School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK)

  • Ralph Gottschalg

    (Centre for Renewable Energy Systems Technology, School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK)

Abstract

Plane-of-array (PoA) irradiation data is a requirement to simulate the energetic performance of photovoltaic devices (PVs). Normally, solar data is only available as global horizontal irradiation, for a limited number of locations, and typically in hourly time resolution. One approach to handling this restricted data is to enhance it initially by interpolation to the location of interest; next, it must be translated to PoA data by separately considering the diffuse and the beam components. There are many methods of interpolation. This research selects ordinary kriging as the best performing technique by studying mathematical properties, experimentation and leave-one-out-cross validation. Likewise, a number of different translation models has been developed, most of them parameterised for specific measurement setups and locations. The work presented identifies the optimum approach for the UK on a national scale. The global horizontal irradiation will be split into its constituent parts. Divers separation models were tried. The results of each separation algorithm were checked against measured data distributed across the UK. It became apparent that while there is little difference between procedures (14 Wh/m 2 mean bias error ( MBE ), 12 Wh/m 2 root mean square error ( RMSE )), the Ridley, Boland, Lauret equation (a universal split algorithm) consistently performed well. The combined interpolation/separation RMSE is 86 Wh/m 2 ).

Suggested Citation

  • Diane Palmer & Ian Cole & Tom Betts & Ralph Gottschalg, 2017. "Interpolating and Estimating Horizontal Diffuse Solar Irradiation to Provide UK-Wide Coverage: Selection of the Best Performing Models," Energies, MDPI, vol. 10(2), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:181-:d:89438
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    References listed on IDEAS

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    1. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    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.
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    Citations

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    Cited by:

    1. Diane Palmer & Elena Koubli & Tom Betts & Ralph Gottschalg, 2017. "The UK Solar Farm Fleet: A Challenge for the National Grid? †," Energies, MDPI, vol. 10(8), pages 1-22, August.
    2. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    3. Sarmiento, Nilsa & Belmonte, Silvina & Dellicompagni, Pablo & Franco, Judith & Escalante, Karina & Sarmiento, Joaquín, 2019. "A solar irradiation GIS as decision support tool for the Province of Salta, Argentina," Renewable Energy, Elsevier, vol. 132(C), pages 68-80.
    4. Palmer, Diane & Gottschalg, Ralph & Betts, Tom, 2019. "The future scope of large-scale solar in the UK: Site suitability and target analysis," Renewable Energy, Elsevier, vol. 133(C), pages 1136-1146.
    5. Daniela Malcangio & Claudio Donvito & Nicola Ungaro, 2018. "Statistical Analysis of Bathing Water Quality in Puglia Region (Italy)," IJERPH, MDPI, vol. 15(5), pages 1-15, May.
    6. YoungHyun Koo & Myeongchan Oh & Sung-Min Kim & Hyeong-Dong Park, 2020. "Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-19, January.
    7. Schepel, Veikko & Tozzi, Arianna & Klement, Marianne & Ziar, Hesan & Isabella, Olindo & Zeman, Miro, 2020. "The Dutch PV portal 2.0: An online photovoltaic performance modeling environment for the Netherlands," Renewable Energy, Elsevier, vol. 154(C), pages 175-186.
    8. Moretón, R. & Lorenzo, E. & Pinto, A. & Muñoz, J. & Narvarte, L., 2017. "From broadband horizontal to effective in-plane irradiation: A review of modelling and derived uncertainty for PV yield prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 886-903.
    9. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.
    10. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.

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