On-Line Electrical Supply Generation Fuel Mix Data Analysis using Python and TensorFlow
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
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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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/openAccess2024-07-04T19:11:56Zoai:repositorio.unesp.br:11449/232958Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:26:53.293218Repositório 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_ |
1808129522435883008 |