eprintid: 17379
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/73/79
datestamp: 2023-12-19 03:23:46
lastmod: 2023-12-19 03:23:46
status_changed: 2023-12-19 03:07:57
type: article
metadata_visibility: show
creators_name: Masood, F.
creators_name: Nallagownden, P.
creators_name: Elamvazuthi, I.
creators_name: Akhter, J.
creators_name: Alam, M.A.
title: A Review of Artificial Intelligence-Based Techniques to Estimate Atmospheric Parameters Influencing the Performance of Concentrating Photovoltaic/Thermal Systems
ispublished: pub
keywords: Atmospheric temperature; Fossil fuels; Intelligent systems; Neural networks; Parameter estimation; Solar cells; Solar power generation; Solar radiation, Atmospheric parameters; Concentrating photovoltaic; Concentrating photovoltaic/thermal system; Performance; Photovoltaic thermals; Photovoltaic/thermal systems; Renewable energies; Solar cell materials; Solar irradiances; Thermal technologies, Genetic algorithms
note: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
abstract: Concentrating photovoltaic/thermal (CPV/T) technology is regarded as the most auspicious part of renewable energy capable of reducing reliance on fossil fuels due to its superior performance and hybrid output nature. CPV/T technology aims to reduce the cost of the renewable systems by replacing the costly solar cell material with relatively cheap optical devices that concentrate the light collected from the sun to a small solar PV cell and simultaneously generating useful heat energy for process heat applications. However, the electrical and thermal performances of systems utilizing the methodology mentioned above get strongly affected by atmospheric parameters like solar radiation, ambient temperature, and the solar spectrum. In recent years, due to the advantages tendered by Artificial Intelligence tools to solve ambiguous and non-linear problems, many authors have used intelligent system-based techniques for the prediction of the above-mentioned atmospheric parameters. This paper presents a review of artificial intelligence-based techniques, including Artificial Neural Network, Genetic Algorithm, and their composite models for the estimation of atmospheric parameters that significantly influence the working of hybrid concentrating PV/thermal systems. The review demonstrates the feasibility and accuracy of artificial intelligence-based tools for precise solar insolation and ambient air temperature prediction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
date: 2022
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142747414&doi=10.1007%2f978-981-16-2183-3_35&partnerID=40&md5=9071d17734c020163a7819c160ab0f6a
id_number: 10.1007/978-981-16-2183-3₃₅
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 758
pagerange: 365-372
refereed: TRUE
isbn: 9789811621826
issn: 18761100
citation:   Masood, F. and Nallagownden, P. and Elamvazuthi, I. and Akhter, J. and Alam, M.A.  (2022) A Review of Artificial Intelligence-Based Techniques to Estimate Atmospheric Parameters Influencing the Performance of Concentrating Photovoltaic/Thermal Systems.  Lecture Notes in Electrical Engineering, 758.  pp. 365-372.  ISSN 18761100