A simple and reliable empirical model with two predictors for estimating 1-minute diffuse fraction. (1st March 2019)

• Record Type: Journal Article
• Authors: Paulescu, Eugenia; Blaga, Robert
• Is Part Of: Solar energy. Volume 180(2019)
• Page Start: 75
• Page End: 84
• Publication Date: 2019-03-01
• Subjects: Diffuse fraction -- Direct-normal solar irradiance -- Empirical separation model -- Baseline Surface Radiation Network (BSRN); Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2019.01.029
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: A new separation model for 1-minute diffuse fraction is proposed. It shows excellent performance despite its structural simplicity. Model performances are highly dependent on the local climate. Direct-normal irradiance is estimated most accurately at arid locations. Abstract: Recent progress in radiometry allows the development of diffuse fraction models based on data stored at high-frequency sampling. At the present only a handful of such models exist in the literature. In this paper, we present a new and simple model for estimating diffuse fraction developed on 1-minute resolution data. The new model (PB) is extensively validated on data from the Baseline Surface Radiation Network, for both diffuse fraction ( kd ) and direct normal irradiance ( Gn ). The results show that PB performs well compared to other similar models, despite its structural simplicity. kd is estimated with the best accuracy in locations with temperate climate (TM), the average (median) nRMSE of the tested TM stations being 18.38% (17.79%). The Gn estimates are found to be most accurate at arid locations (AR), the average (median) nRMSE over all tested AR stations being 17.29% (16.96%). We find that the performance of the models varies strongly as a function of the climate zone or when we switch between estimating diffuse and direct normal irradiance components, possibly changing the accuracy-based rankings among models, a fact which should be taken into consideration by users when selecting appropriate models.