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Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes

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  • Yang, Dazhi
  • Gueymard, Christian A.

Abstract

With the objective of producing high-resolution and high-accuracy maps of mean annual irradiance at country scale, this contribution exploits the complementary properties of two distinct sources of solar irradiance data: gridded modeled data derived from satellite observations, and station-specific typical meteorological year (TMY) data. A data fusion procedure based on Gaussian process modeling is used to optimally combine the two sources of data and derive solar resource maps. Gridded physical solar model version 3 (PSM3) satellite-derived data at 4-km resolution and TMY3 data from 67 stations in California are used to produce a map of mean annual global horizontal irradiance at 2-km resolution and exemplify the procedure. It is shown that by integrating the PSM3 data with TMY3 data, the original 5.2% mean error in the PSM3 map is reduced to 1.6%. The demonstrated approach is suitable for a variety of regional-scale applications for which both high-resolution data of low accuracy and low-resolution measurements of high accuracy are available.

Suggested Citation

  • Yang, Dazhi & Gueymard, Christian A., 2019. "Producing high-quality solar resource maps by integrating high- and low-accuracy measurements using Gaussian processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    • Handle: RePEc:eee:rensus:v:113:y:2019:i:c:45
    DOI: 10.1016/j.rser.2019.109260
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    Cited by:

    1. Quan, Hao & Yang, Dazhi, 2020. "Probabilistic solar irradiance transposition models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 125(C).
    2. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    4. Mayer, Martin János & Yang, Dazhi, 2022. "Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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