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

Dazhi Yang and Christian A. Gueymard

Renewable and Sustainable Energy Reviews, 2019, vol. 113, issue C, -

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.

Keywords: Gaussian process; Data fusion; Solar resource; Kriging; Mapping (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.rser.2019.109260

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