Effect of Feature Selection on the Prediction of Direct Normal Irradiance
Solar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different sol...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020003 |
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| author | Mohamed Khalifa Boutahir Yousef Farhaoui Mourade Azrour Imad Zeroual Ahmad El Allaoui |
| author_facet | Mohamed Khalifa Boutahir Yousef Farhaoui Mourade Azrour Imad Zeroual Ahmad El Allaoui |
| author_sort | Mohamed Khalifa Boutahir |
| collection | DOAJ |
| description | Solar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different solar radiation components, such as Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI). Unfortunately, measurements of solar radiation are not easily accessible for the majority of regions on the globe. This paper aims to develop a set of deep learning models through feature importance algorithms to predict the DNI data. The proposed models are based on historical data of meteorological parameters and solar radiation properties in a specific location of the region of Errachidia, Morocco, from January 1, 2017, to December 31, 2019, with an interval of 60 minutes. The findings demonstrated that feature selection approaches play a crucial role in forecasting of solar radiation accurately when compared with the available data. |
| format | Article |
| id | doaj-art-5f56bdb2185b417c88e0e75af65ea737 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-5f56bdb2185b417c88e0e75af65ea7372025-02-02T06:14:04ZengTsinghua University PressBig Data Mining and Analytics2096-06542022-12-015430931710.26599/BDMA.2022.9020003Effect of Feature Selection on the Prediction of Direct Normal IrradianceMohamed Khalifa Boutahir0Yousef Farhaoui1Mourade Azrour2Imad Zeroual3Ahmad El Allaoui4Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, MoroccoFaculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, MoroccoFaculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, MoroccoFaculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, MoroccoFaculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, MoroccoSolar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different solar radiation components, such as Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI). Unfortunately, measurements of solar radiation are not easily accessible for the majority of regions on the globe. This paper aims to develop a set of deep learning models through feature importance algorithms to predict the DNI data. The proposed models are based on historical data of meteorological parameters and solar radiation properties in a specific location of the region of Errachidia, Morocco, from January 1, 2017, to December 31, 2019, with an interval of 60 minutes. The findings demonstrated that feature selection approaches play a crucial role in forecasting of solar radiation accurately when compared with the available data.https://www.sciopen.com/article/10.26599/BDMA.2022.9020003machine learningdeep learningfeature importancerenewable energiessolar radiation |
| spellingShingle | Mohamed Khalifa Boutahir Yousef Farhaoui Mourade Azrour Imad Zeroual Ahmad El Allaoui Effect of Feature Selection on the Prediction of Direct Normal Irradiance Big Data Mining and Analytics machine learning deep learning feature importance renewable energies solar radiation |
| title | Effect of Feature Selection on the Prediction of Direct Normal Irradiance |
| title_full | Effect of Feature Selection on the Prediction of Direct Normal Irradiance |
| title_fullStr | Effect of Feature Selection on the Prediction of Direct Normal Irradiance |
| title_full_unstemmed | Effect of Feature Selection on the Prediction of Direct Normal Irradiance |
| title_short | Effect of Feature Selection on the Prediction of Direct Normal Irradiance |
| title_sort | effect of feature selection on the prediction of direct normal irradiance |
| topic | machine learning deep learning feature importance renewable energies solar radiation |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020003 |
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