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Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm
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Khosravi, A., Malekan, M., Pabon, J. J. G., Zhao, Xiaowei and Assad, M. E. H. (2020) Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm. Journal of Cleaner Production, 244 . 118904. doi:10.1016/j.jclepro.2019.118904 ISSN 0959-6526.
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WRAP-design-parameter-modeling-solar-power-neuro-fuzzy-Zhao-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (3261Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.jclepro.2019.118904
Abstract
Determining the optimal sizing of a solar power tower system (SPTS) with a thermal energy storage system is subject to finding the optimum values of design parameters including the solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours. These design parameters are determined for each station separately and have remarkable effects on the thermo-economic performance of the system. This paper aims to demonstrate how artificial intelligence (AI) techniques may play an important role in addressing the above-mentioned need and help determine the optimum design parameters for different stations. For this purpose, we developed a thermo-economic model of a 100 MW SPTS with a molten salt storage system for five stations (two stations in India, and one each in Bangladesh, Pakistan, and Afghanistan). A method-based AI is utilized in this paper to ascertain the design parameters of the system. Additionally, a novel hybrid method based on adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching-learning-based optimization algorithm (ANFIS-GATLBO) is employed. The input parameters are latitude, longitude, design point DNI and SM, while the annual energy produced, levelized cost of energy and capacity factor are the target variables. The results of the study show that although the annual energy produced by SPTS rises by increasing the SM and decreasing design point DNI, optimum design parameters should be determined by the economic factors. In addition, it was found that the ANFIS-GATLBO method used in this study successfully predicted the targets with a correlation coefficient close to 1.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Solar power plants, Solar thermal energy , Fuzzy systems , Genetic algorithms, Computer algorithms, Machine learning, Artificial intelligence | ||||||||
Journal or Publication Title: | Journal of Cleaner Production | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 0959-6526 | ||||||||
Official Date: | 20 January 2020 | ||||||||
Dates: |
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Volume: | 244 | ||||||||
Article Number: | 118904 | ||||||||
DOI: | 10.1016/j.jclepro.2019.118904 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | Elsevier | ||||||||
Date of first compliant deposit: | 3 January 2020 | ||||||||
Date of first compliant Open Access: | 17 October 2020 | ||||||||
RIOXX Funder/Project Grant: |
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