On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow

Detalhes bibliográficos
Autor(a) principal: Grout, Ian
Data de Publicação: 2019
Outros Autores: De Ferreira, Willian Assis Pedrobon [UNESP], Silva, Alexandre Cesar Rodrigues Da [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICPEI47862.2019.8944972
http://hdl.handle.net/11449/232958
Resumo: In this paper, the Python scripting language and TensorFlow open source platform for machine learning is used to create a software script that can automatically extract electricity supply generation data from an on-line resource and use machine learning techniques to analyze the available data for the creation of end-user information. An on-line resource was chosen where the data could be readily extracted and stored in multi-dimensional TensorFlow arrays for analysis. The usefulness of such generated end-user information is however based on the accuracy of the information and any biases introduced in the data collation, data presentation, data analysis and results presentation, along with the perceptions of the enduser. With these considerations in mind, this paper focuses on the aspects relating to the creation, operation and use of the Python and TensorFlow script.
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spelling On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlowanalysiselectricity supply generationmonitoringon-linePythonTensorFlowIn this paper, the Python scripting language and TensorFlow open source platform for machine learning is used to create a software script that can automatically extract electricity supply generation data from an on-line resource and use machine learning techniques to analyze the available data for the creation of end-user information. An on-line resource was chosen where the data could be readily extracted and stored in multi-dimensional TensorFlow arrays for analysis. The usefulness of such generated end-user information is however based on the accuracy of the information and any biases introduced in the data collation, data presentation, data analysis and results presentation, along with the perceptions of the enduser. With these considerations in mind, this paper focuses on the aspects relating to the creation, operation and use of the Python and TensorFlow script.University of Limerick Department of Electronic and Computer EngineeringFaculdade de Engenharia Universidade Estadual PaulistaFaculdade de Engenharia Universidade Estadual PaulistaUniversity of LimerickUniversidade Estadual Paulista (UNESP)Grout, IanDe Ferreira, Willian Assis Pedrobon [UNESP]Silva, Alexandre Cesar Rodrigues Da [UNESP]2022-04-30T22:28:34Z2022-04-30T22:28:34Z2019-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject20-23http://dx.doi.org/10.1109/ICPEI47862.2019.8944972Proceedings of the 2019 International Conference on Power, Energy and Innovations, ICPEI 2019, p. 20-23.http://hdl.handle.net/11449/23295810.1109/ICPEI47862.2019.89449722-s2.0-85078186896Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the 2019 International Conference on Power, Energy and Innovations, ICPEI 2019info:eu-repo/semantics/openAccess2022-04-30T22:28:34Zoai:repositorio.unesp.br:11449/232958Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-05-23T21:10:43.717876Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
title On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
spellingShingle On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
Grout, Ian
analysis
electricity supply generation
monitoring
on-line
Python
TensorFlow
title_short On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
title_full On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
title_fullStr On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
title_full_unstemmed On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
title_sort On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
author Grout, Ian
author_facet Grout, Ian
De Ferreira, Willian Assis Pedrobon [UNESP]
Silva, Alexandre Cesar Rodrigues Da [UNESP]
author_role author
author2 De Ferreira, Willian Assis Pedrobon [UNESP]
Silva, Alexandre Cesar Rodrigues Da [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv University of Limerick
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Grout, Ian
De Ferreira, Willian Assis Pedrobon [UNESP]
Silva, Alexandre Cesar Rodrigues Da [UNESP]
dc.subject.por.fl_str_mv analysis
electricity supply generation
monitoring
on-line
Python
TensorFlow
topic analysis
electricity supply generation
monitoring
on-line
Python
TensorFlow
description In this paper, the Python scripting language and TensorFlow open source platform for machine learning is used to create a software script that can automatically extract electricity supply generation data from an on-line resource and use machine learning techniques to analyze the available data for the creation of end-user information. An on-line resource was chosen where the data could be readily extracted and stored in multi-dimensional TensorFlow arrays for analysis. The usefulness of such generated end-user information is however based on the accuracy of the information and any biases introduced in the data collation, data presentation, data analysis and results presentation, along with the perceptions of the enduser. With these considerations in mind, this paper focuses on the aspects relating to the creation, operation and use of the Python and TensorFlow script.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-01
2022-04-30T22:28:34Z
2022-04-30T22:28:34Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ICPEI47862.2019.8944972
Proceedings of the 2019 International Conference on Power, Energy and Innovations, ICPEI 2019, p. 20-23.
http://hdl.handle.net/11449/232958
10.1109/ICPEI47862.2019.8944972
2-s2.0-85078186896
url http://dx.doi.org/10.1109/ICPEI47862.2019.8944972
http://hdl.handle.net/11449/232958
identifier_str_mv Proceedings of the 2019 International Conference on Power, Energy and Innovations, ICPEI 2019, p. 20-23.
10.1109/ICPEI47862.2019.8944972
2-s2.0-85078186896
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the 2019 International Conference on Power, Energy and Innovations, ICPEI 2019
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20-23
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)
repository.mail.fl_str_mv
_version_ 1803045723958149120

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