An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation

With the expansion of grid-connected solar power generation, the variability of photovoltaic power generation has become increasingly pronounced. Accurate photovoltaic output prediction is necessary to ensure power system stability. In this work, an inertia weighting strategy and the Cauchy mutation...

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Main Authors: Lin, Guo Qian, Li, Ling Ling, Tseng, Ming Lang, Liu, Han Min, Yuan, Dong Dong, Tan, Raymond Girard R.
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Published: Animo Repository 2020
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/4134
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-51912021-12-03T01:48:42Z An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation Lin, Guo Qian Li, Ling Ling Tseng, Ming Lang Liu, Han Min Yuan, Dong Dong Tan, Raymond Girard R. With the expansion of grid-connected solar power generation, the variability of photovoltaic power generation has become increasingly pronounced. Accurate photovoltaic output prediction is necessary to ensure power system stability. In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. The former balances the search and mining capabilities at the population location search equation, and the latter helps to increase the diversity of the masses and to void avoid entrapment into local optima. Various meteorological conditions affecting the photovoltaic power generation are discussed and the experimental input data is optimized by grey relational analysis. The results using multiple test functions and the real data of photovoltaic power station in Australia have verified that the proposed model has better optimization performance compared with other models. The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid, and maintains the system reliability. © 2020 2020-04-20T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/4134 info:doi/10.1016/j.jclepro.2020.119966 Faculty Research Work Animo Repository Photovoltaic power generation Support vector machines Chemical Engineering Energy Systems
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Photovoltaic power generation
Support vector machines
Chemical Engineering
Energy Systems
spellingShingle Photovoltaic power generation
Support vector machines
Chemical Engineering
Energy Systems
Lin, Guo Qian
Li, Ling Ling
Tseng, Ming Lang
Liu, Han Min
Yuan, Dong Dong
Tan, Raymond Girard R.
An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
description With the expansion of grid-connected solar power generation, the variability of photovoltaic power generation has become increasingly pronounced. Accurate photovoltaic output prediction is necessary to ensure power system stability. In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. The former balances the search and mining capabilities at the population location search equation, and the latter helps to increase the diversity of the masses and to void avoid entrapment into local optima. Various meteorological conditions affecting the photovoltaic power generation are discussed and the experimental input data is optimized by grey relational analysis. The results using multiple test functions and the real data of photovoltaic power station in Australia have verified that the proposed model has better optimization performance compared with other models. The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid, and maintains the system reliability. © 2020
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author Lin, Guo Qian
Li, Ling Ling
Tseng, Ming Lang
Liu, Han Min
Yuan, Dong Dong
Tan, Raymond Girard R.
author_facet Lin, Guo Qian
Li, Ling Ling
Tseng, Ming Lang
Liu, Han Min
Yuan, Dong Dong
Tan, Raymond Girard R.
author_sort Lin, Guo Qian
title An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
title_short An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
title_full An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
title_fullStr An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
title_full_unstemmed An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
title_sort improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
publisher Animo Repository
publishDate 2020
url https://animorepository.dlsu.edu.ph/faculty_research/4134
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