Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models
In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/3/1473 |
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| author | M. I. Dieste-Velasco S. García-Rodríguez A. García-Rodríguez M. Díez-Mediavilla C. Alonso-Tristán |
| author_facet | M. I. Dieste-Velasco S. García-Rodríguez A. García-Rodríguez M. Díez-Mediavilla C. Alonso-Tristán |
| author_sort | M. I. Dieste-Velasco |
| collection | DOAJ |
| description | In the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="normal">I</mi><mrow><mi>UV</mi></mrow></msub></mrow></semantics></math></inline-formula>), 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><msub><mi mathvariant="normal">I</mi><mrow><mi>GH</mi></mrow></msub></mrow></semantics></math></inline-formula>), clearness index <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><msub><mi mathvariant="normal">k</mi><mi mathvariant="normal">t</mi></msub></mrow></semantics></math></inline-formula>), solar altitude angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi mathvariant="sans-serif">α</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, horizontal beam irradiance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi mathvariant="normal">I</mi><mrow><mi>BH</mi></mrow></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, diffuse fraction (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">D</mi></mrow></semantics></math></inline-formula>), temperature <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mi mathvariant="normal">T</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, sky clearness <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi mathvariant="sans-serif">ε</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, cloud cover <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi>Cc</mi></mrow></semantics></math></inline-formula>), horizontal diffuse irradiance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi mathvariant="normal">I</mi><mrow><mi>DH</mi></mrow></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models. |
| first_indexed | 2024-03-11T09:53:28Z |
| format | Article |
| id | doaj.art-df492991464f48b0ba79253e1666868f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:53:28Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-df492991464f48b0ba79253e1666868f2023-11-16T16:06:00ZengMDPI AGApplied Sciences2076-34172023-01-01133147310.3390/app13031473Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression ModelsM. I. Dieste-Velasco0S. García-Rodríguez1A. García-Rodríguez2M. Díez-Mediavilla3C. Alonso-Tristán4Research Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, Universidad de Burgos, 09006 Burgos, SpainResearch Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, Universidad de Burgos, 09006 Burgos, SpainResearch Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, Universidad de Burgos, 09006 Burgos, SpainResearch Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, Universidad de Burgos, 09006 Burgos, SpainResearch Group Solar and Wind Feasibility Technologies (SWIFT), Electromechanical Engineering Department, Universidad de Burgos, 09006 Burgos, SpainIn the present study, different models constructed with meteorological variables are proposed for the determination of horizontal ultraviolet irradiance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi mathvariant="normal">I</mi><mrow><mi>UV</mi></mrow></msub></mrow></semantics></math></inline-formula>), 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><msub><mi mathvariant="normal">I</mi><mrow><mi>GH</mi></mrow></msub></mrow></semantics></math></inline-formula>), clearness index <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><msub><mi mathvariant="normal">k</mi><mi mathvariant="normal">t</mi></msub></mrow></semantics></math></inline-formula>), solar altitude angle <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi mathvariant="sans-serif">α</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, horizontal beam irradiance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi mathvariant="normal">I</mi><mrow><mi>BH</mi></mrow></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, diffuse fraction (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">D</mi></mrow></semantics></math></inline-formula>), temperature <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mi mathvariant="normal">T</mi><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, sky clearness <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi mathvariant="sans-serif">ε</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, cloud cover <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo stretchy="false">(</mo><mi>Cc</mi></mrow></semantics></math></inline-formula>), horizontal diffuse irradiance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>(</mo><mrow><msub><mi mathvariant="normal">I</mi><mrow><mi>DH</mi></mrow></msub></mrow><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, and sky brightness (Δ). The ANN models yielded results of greater accuracy than the regression models.https://www.mdpi.com/2076-3417/13/3/1473UV irradianceANNmodelingmultilinear regression models |
| spellingShingle | M. I. Dieste-Velasco S. García-Rodríguez A. García-Rodríguez M. Díez-Mediavilla C. Alonso-Tristán Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models Applied Sciences UV irradiance ANN modeling multilinear regression models |
| title | Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models |
| title_full | Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models |
| title_fullStr | Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models |
| title_full_unstemmed | Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models |
| title_short | Modeling Horizontal Ultraviolet Irradiance for All Sky Conditions by Using Artificial Neural Networks and Regression Models |
| title_sort | modeling horizontal ultraviolet irradiance for all sky conditions by using artificial neural networks and regression models |
| topic | UV irradiance ANN modeling multilinear regression models |
| url | https://www.mdpi.com/2076-3417/13/3/1473 |
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