Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models
Source abstract
Abstract
In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (IUV), on the basis of data collected at Burgos (Spain) during an experimental campaign between March 2020 and May 2022. The aim is to explore the effectiveness of a range of variables for modelling horizontal ultraviolet irradiance through a comparison of supervised artificial neural network (ANN) and regression model results. A preliminary feature selection process using the Pearson correlation coefficient was sufficient to determine the variables for use in the models. The following variables and their influence on horizontal ultraviolet irradiance were analyzed: horizontal global irradiance (IGH), clearness index (kt), solar altitude angle (α), horizontal beam irradiance (IBH), diffuse fraction (D), temperature (T), sky clearness (ε), cloud cover (Cc), horizontal diffuse irradiance (IDH), and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models.
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Research context
OpenAlex topics: Urban Heat Island Mitigation · Atmospheric Ozone and Climate · Impact of Light on Environment and Health
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, Universidad de Burgos, 09006 Burgos, Spain
Funding
Spanish Ministry of Science · MCIN/AEI/10.13039/501100011033 · Junta de Castilla y León
References (41)
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