Effect of Feature Selection on the Prediction of Direct Normal Irradiance

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2022-07-18 DOI:10.26599/BDMA.2022.9020003

Mohamed Khalifa Boutahir;Yousef Farhaoui;Mourade Azrour;Imad Zeroual;Ahmad El Allaoui

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引用次数: 3

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.

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特征选择对直接法向辐照度预测的影响

太阳辐射能够产生热量、引起化学反应或发电。因此，必须确定一天中不同时间的太阳辐射量，以设计和装备所有太阳能系统。此外，有必要深入了解不同的太阳辐射成分，如直接法向辐照度（DNI）、漫反射水平辐照度（DHI）和全局水平辐照度（GHI）。不幸的是，对全球大多数地区来说，太阳辐射的测量并不容易。本文旨在通过特征重要性算法开发一组深度学习模型来预测DNI数据。所提出的模型基于2017年1月1日至2019年12月31日摩洛哥埃拉希迪亚地区特定位置的气象参数和太阳辐射特性的历史数据，时间间隔为60分钟。研究结果表明，与现有数据相比，特征选择方法在准确预测太阳辐射方面发挥着至关重要的作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.