Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations

Detalhes bibliográficos
Autor(a) principal: Lourenço, Filipe Vieira
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149810
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing LocationsSustainabilityReportingCRISP-DM MethodologyData MiningUnsupervised LearningClusteringInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsWith the increase of amount of data generated, finding a way to get knowledge and insights from them is fundamental in decision-making for any kind company in any field. For a company as EDP, an electric utility, and its responsibilities and commitments entailed its crucial to meet the expectations of its customers and investors, otherwise it is in danger of losing its reputation and money to its competitors. With this in mind and being that I was in the team responsible for collecting the sustainable data from the locations belonging to EDP Group, we decided to move forward to an analysis of the reporting process and by complementing it by analysing a clustering solution to characterize the diverse locations. This project followed CRISP-DM methodology from the beginning until the end where the clustering solution was found by applying hierarchical clustering on top of K-means. All the data understanding, transformation, modeling and model evaluation was performed using Jupyter Notebook, being the final solution built in Power BI platform.Neto, Miguel de Castro Simões FerreiraRUNLourenço, Filipe Vieira2023-02-28T15:57:10Z2023-01-262023-01-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149810TID:203238834enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:31:40Zoai:run.unl.pt:10362/149810Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:52.553203Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
title Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
spellingShingle Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
Lourenço, Filipe Vieira
Sustainability
Reporting
CRISP-DM Methodology
Data Mining
Unsupervised Learning
Clustering
title_short Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
title_full Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
title_fullStr Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
title_full_unstemmed Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
title_sort Multivariate Analysis of Sustainable Data in Energy Utility - Clustering Analysis Application in Contributing Locations
author Lourenço, Filipe Vieira
author_facet Lourenço, Filipe Vieira
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
RUN
dc.contributor.author.fl_str_mv Lourenço, Filipe Vieira
dc.subject.por.fl_str_mv Sustainability
Reporting
CRISP-DM Methodology
Data Mining
Unsupervised Learning
Clustering
topic Sustainability
Reporting
CRISP-DM Methodology
Data Mining
Unsupervised Learning
Clustering
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-02-28T15:57:10Z
2023-01-26
2023-01-26T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/149810
TID:203238834
url http://hdl.handle.net/10362/149810
identifier_str_mv TID:203238834
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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