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Open Access

Effect of Feature Selection on the Prediction of Direct Normal Irradiance

Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia 52000, Morocco
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Abstract

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.

Keywords

machine learningdeep learningfeature importancerenewable energiessolar radiation

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Big Data Mining and Analytics
Volume 5 Issue 4,
December 2022
Pages 309-317
DOI: 10.26599/BDMA.2022.9020003
Cite this article:
Boutahir MK, Farhaoui Y, Azrour M, et al. Effect of Feature Selection on the Prediction of Direct Normal Irradiance. Big Data Mining and Analytics, 2022, 5(4): 309-317. https://doi.org/10.26599/BDMA.2022.9020003

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Received: 31 December 2021
Accepted: 24 January 2022
Published: 18 July 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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