The Most Adjusted Predictive Models for Energy Costs
Autor(a) principal: | |
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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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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 |
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mluisa.alvim@gmail.com |
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1817548712241528832 |