Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM

Detalhes bibliográficos
Autor(a) principal: Bonventi, Waldemar
Data de Publicação: 2023
Outros Autores: Godoy, Eduardo P [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1063/5.0127408
http://hdl.handle.net/11449/247020
Resumo: The use of renewable energy, notably solar and wind energy, has grown exponentially in Brazil. Consumers can generate their energy using renewable sources, whether interconnected to the distribution system (on-grid) or not (off-grid). In this paper, a fuzzy method is developed for the recommendation of solar and wind sources, for any location in the Brazilian territory. In many aspects, it can be viewed as a representation of human decision-making using sets and inference rules and also can be with vagueness and uncertainty, being very useful to idealize recommendation systems. Georeferenced and historical data were obtained from 2003 to 2019 on solar irradiation and wind speed, and electricity consumption until 2021. With the energy generation data from photovoltaic panels and wind turbines, this method allows us to propose installed areas by each technology and obtain the membership of fuzzy recommendation between solar, wind, both solar and wind, unfeasible or hybrid. In addition, a long short-term memory neural network and the seasonal autoregressive integrated moving average model were used to predict consumption for more than 30 months ahead, allowing the recalculation of fuzzy memberships and updating the installation area by respective technologies. As a result, the recommendation is given as the installed area (m2) of each technology per km2 of consumer units, as a function of the regional consumption density (MWh/km2). It can be concluded that it is possible to plan the viability of the type of renewable energy used, according to regional characteristics for smaller consumer units (farms, cooperatives, industries, consortiums), given the diversity of these factors in the huge Brazilian territory. This methodology is in line with the Brazilian Normative Resolution that authorizes the generation of energy by landowners.
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spelling Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTMThe use of renewable energy, notably solar and wind energy, has grown exponentially in Brazil. Consumers can generate their energy using renewable sources, whether interconnected to the distribution system (on-grid) or not (off-grid). In this paper, a fuzzy method is developed for the recommendation of solar and wind sources, for any location in the Brazilian territory. In many aspects, it can be viewed as a representation of human decision-making using sets and inference rules and also can be with vagueness and uncertainty, being very useful to idealize recommendation systems. Georeferenced and historical data were obtained from 2003 to 2019 on solar irradiation and wind speed, and electricity consumption until 2021. With the energy generation data from photovoltaic panels and wind turbines, this method allows us to propose installed areas by each technology and obtain the membership of fuzzy recommendation between solar, wind, both solar and wind, unfeasible or hybrid. In addition, a long short-term memory neural network and the seasonal autoregressive integrated moving average model were used to predict consumption for more than 30 months ahead, allowing the recalculation of fuzzy memberships and updating the installation area by respective technologies. As a result, the recommendation is given as the installed area (m2) of each technology per km2 of consumer units, as a function of the regional consumption density (MWh/km2). It can be concluded that it is possible to plan the viability of the type of renewable energy used, according to regional characteristics for smaller consumer units (farms, cooperatives, industries, consortiums), given the diversity of these factors in the huge Brazilian territory. This methodology is in line with the Brazilian Normative Resolution that authorizes the generation of energy by landowners.Faculdade de Tecnologia de Sorocaba (Fatec-So), SPUniversidade Estadual Paulista (Unesp), SPUniversidade Estadual Paulista (Unesp), SPFaculdade de Tecnologia de Sorocaba (Fatec-So)Universidade Estadual Paulista (UNESP)Bonventi, WaldemarGodoy, Eduardo P [UNESP]2023-07-29T12:56:53Z2023-07-29T12:56:53Z2023-03-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1063/5.0127408Journal of Renewable and Sustainable Energy, v. 15, n. 2, 2023.1941-7012http://hdl.handle.net/11449/24702010.1063/5.01274082-s2.0-85150395660Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Renewable and Sustainable Energyinfo:eu-repo/semantics/openAccess2023-07-29T12:56:53Zoai:repositorio.unesp.br:11449/247020Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:46:47.157403Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
title Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
spellingShingle Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
Bonventi, Waldemar
title_short Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
title_full Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
title_fullStr Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
title_full_unstemmed Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
title_sort Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM
author Bonventi, Waldemar
author_facet Bonventi, Waldemar
Godoy, Eduardo P [UNESP]
author_role author
author2 Godoy, Eduardo P [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Faculdade de Tecnologia de Sorocaba (Fatec-So)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Bonventi, Waldemar
Godoy, Eduardo P [UNESP]
description The use of renewable energy, notably solar and wind energy, has grown exponentially in Brazil. Consumers can generate their energy using renewable sources, whether interconnected to the distribution system (on-grid) or not (off-grid). In this paper, a fuzzy method is developed for the recommendation of solar and wind sources, for any location in the Brazilian territory. In many aspects, it can be viewed as a representation of human decision-making using sets and inference rules and also can be with vagueness and uncertainty, being very useful to idealize recommendation systems. Georeferenced and historical data were obtained from 2003 to 2019 on solar irradiation and wind speed, and electricity consumption until 2021. With the energy generation data from photovoltaic panels and wind turbines, this method allows us to propose installed areas by each technology and obtain the membership of fuzzy recommendation between solar, wind, both solar and wind, unfeasible or hybrid. In addition, a long short-term memory neural network and the seasonal autoregressive integrated moving average model were used to predict consumption for more than 30 months ahead, allowing the recalculation of fuzzy memberships and updating the installation area by respective technologies. As a result, the recommendation is given as the installed area (m2) of each technology per km2 of consumer units, as a function of the regional consumption density (MWh/km2). It can be concluded that it is possible to plan the viability of the type of renewable energy used, according to regional characteristics for smaller consumer units (farms, cooperatives, industries, consortiums), given the diversity of these factors in the huge Brazilian territory. This methodology is in line with the Brazilian Normative Resolution that authorizes the generation of energy by landowners.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T12:56:53Z
2023-07-29T12:56:53Z
2023-03-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1063/5.0127408
Journal of Renewable and Sustainable Energy, v. 15, n. 2, 2023.
1941-7012
http://hdl.handle.net/11449/247020
10.1063/5.0127408
2-s2.0-85150395660
url http://dx.doi.org/10.1063/5.0127408
http://hdl.handle.net/11449/247020
identifier_str_mv Journal of Renewable and Sustainable Energy, v. 15, n. 2, 2023.
1941-7012
10.1063/5.0127408
2-s2.0-85150395660
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Renewable and Sustainable Energy
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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