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
Dazhi Yang
Renewable and Sustainable Energy Reviews, 2022, vol. 159, issue C
Abstract:
Separation models, which are used to split beam and diffuse irradiance components from the global one, constitute the largest class of radiation models. Over the years, there have been more than 150 models proposed, and public views on the ranking of these models have been divergent, due to the climate-, weather-, and sky-condition dependency in model performance. In a study conducted in 2016, 140 separation models have been validated using high-quality radiometry data from 54 research-grade stations worldwide, which offer an objective and comprehensive assessment of the then available models. It was found that the Engerer2 model had the best overall performance. Since 2016, numerous other models have been proposed, and most of them are able to claim superiority over Engerer2, once again making the question “what is the best separation model to date” relevant. On this point, this article first reviews these latest advances in separation modeling. Next, as to promote fair comparison, an exceedingly comprehensive benchmarking dataset, which consists of 5 years (2016–2020) of 1-min data, from 126 stations located in all 7 continents and on islands in all 4 oceans, is considered in the empirical part of the article. With this dataset, which has more than 80 million valid 1-min data points, 10 latest separation models with diverse modeling philosophies are compared. It is found that the Yang4 model has the best overall performance, and thus is able to replace Engerer2 as the new quasi-universal separation model.
Keywords: Solar radiation modeling; Separation modeling; Diffuse radiation; Worldwide validation; Benchmarking data; Review (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (18)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:159:y:2022:i:c:s1364032122001174
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DOI: 10.1016/j.rser.2022.112195
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