The Most Adjusted Predictive Models for Energy Costs

Detalhes bibliográficos
Autor(a) principal: Martinho, Vítor
Data de Publicação: 2024
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.19/8633
Resumo: Energy is one of the most important production factors in farms, considering its impact on the profitability of the agricultural sector, its relationship with sustainability and the need for a green transition in agriculture to deal with the challenges created by climate change and the consequent global warming. In the green transition, it is important to replace fossil fuel sources with renewable energies and, in these contexts, the agricultural sector may make a double contribution, producing renewable energy and using more sustainable sources for the different processes and activities in the farms. Taking into account these motivations, this chapter proposes to select the models with better accuracy and the most relevant variables to predict the energy costs in the European Union farming sector. For that, machine learning models were considered, as well as statistical information from European Union databases. This chapter presents useful contributions to better understand the contexts associated with energy cost prediction in European farms.
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spelling The Most Adjusted Predictive Models for Energy CostsDigital Era approachesPredictorsEuropean UnionEnergy is one of the most important production factors in farms, considering its impact on the profitability of the agricultural sector, its relationship with sustainability and the need for a green transition in agriculture to deal with the challenges created by climate change and the consequent global warming. In the green transition, it is important to replace fossil fuel sources with renewable energies and, in these contexts, the agricultural sector may make a double contribution, producing renewable energy and using more sustainable sources for the different processes and activities in the farms. Taking into account these motivations, this chapter proposes to select the models with better accuracy and the most relevant variables to predict the energy costs in the European Union farming sector. For that, machine learning models were considered, as well as statistical information from European Union databases. This chapter presents useful contributions to better understand the contexts associated with energy cost prediction in European farms.Springer, ChamRepositório Científico do Instituto Politécnico de ViseuMartinho, Vítor2024-11-12T14:00:12Z20242024-01-01T00:00:00Zbook partinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.19/8633engMartinho, V.J.P.D. (2024). The Most Adjusted Predictive Models for Energy Costs. In: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-54608-2_710.1007/978-3-031-54608-2_7metadata only accessinfo: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:RCAAP2024-11-16T02:30:44Zoai:repositorio.ipv.pt:10400.19/8633Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-16T02:30:44Repositório 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 The Most Adjusted Predictive Models for Energy Costs
title The Most Adjusted Predictive Models for Energy Costs
spellingShingle The Most Adjusted Predictive Models for Energy Costs
Martinho, Vítor
Digital Era approaches
Predictors
European Union
title_short The Most Adjusted Predictive Models for Energy Costs
title_full The Most Adjusted Predictive Models for Energy Costs
title_fullStr The Most Adjusted Predictive Models for Energy Costs
title_full_unstemmed The Most Adjusted Predictive Models for Energy Costs
title_sort The Most Adjusted Predictive Models for Energy Costs
author Martinho, Vítor
author_facet Martinho, Vítor
author_role author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico de Viseu
dc.contributor.author.fl_str_mv Martinho, Vítor
dc.subject.por.fl_str_mv Digital Era approaches
Predictors
European Union
topic Digital Era approaches
Predictors
European Union
description Energy is one of the most important production factors in farms, considering its impact on the profitability of the agricultural sector, its relationship with sustainability and the need for a green transition in agriculture to deal with the challenges created by climate change and the consequent global warming. In the green transition, it is important to replace fossil fuel sources with renewable energies and, in these contexts, the agricultural sector may make a double contribution, producing renewable energy and using more sustainable sources for the different processes and activities in the farms. Taking into account these motivations, this chapter proposes to select the models with better accuracy and the most relevant variables to predict the energy costs in the European Union farming sector. For that, machine learning models were considered, as well as statistical information from European Union databases. This chapter presents useful contributions to better understand the contexts associated with energy cost prediction in European farms.
publishDate 2024
dc.date.none.fl_str_mv 2024-11-12T14:00:12Z
2024
2024-01-01T00:00:00Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.19/8633
url http://hdl.handle.net/10400.19/8633
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Martinho, V.J.P.D. (2024). The Most Adjusted Predictive Models for Energy Costs. In: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-54608-2_7
10.1007/978-3-031-54608-2_7
dc.rights.driver.fl_str_mv metadata only access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv metadata only access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer, Cham
publisher.none.fl_str_mv Springer, Cham
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 mluisa.alvim@gmail.com
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