Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain

The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of th...

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Autores: García López, Javier, Domínguez Amarillo, Samuel, Sendra, Juan J.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/162499
Acceso en línea:https://hdl.handle.net/11441/162499
https://doi.org/10.3390/buildings14082335
Access Level:acceso abierto
Palabra clave:Andalusia
Bottom-up model
Clustering
Energy model
Open data
Residential energy consumption
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spelling Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, SpainGarcía López, JavierDomínguez Amarillo, SamuelSendra, Juan J.AndalusiaBottom-up modelClusteringEnergy modelOpen dataResidential energy consumptionThe energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock.MDPIConstrucciones Arquitectónicas ITEP130: Arquitectura, Patrimonio y Sostenibilidad: Acústica, Iluminación, Óptica y Energía2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/162499https://doi.org/10.3390/buildings14082335reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésBuildings, 14(8) (2335).https://www.mdpi.com/2075-5309/14/8/2335info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1624992026-06-17T12:51:07Z
dc.title.none.fl_str_mv Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
title Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
spellingShingle Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
García López, Javier
Andalusia
Bottom-up model
Clustering
Energy model
Open data
Residential energy consumption
title_short Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
title_full Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
title_fullStr Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
title_full_unstemmed Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
title_sort Clustering open data for predictive modeling of residential energy consumption across variable scales: a case study in Andalusia, Spain
dc.creator.none.fl_str_mv García López, Javier
Domínguez Amarillo, Samuel
Sendra, Juan J.
author García López, Javier
author_facet García López, Javier
Domínguez Amarillo, Samuel
Sendra, Juan J.
author_role author
author2 Domínguez Amarillo, Samuel
Sendra, Juan J.
author2_role author
author
dc.contributor.none.fl_str_mv Construcciones Arquitectónicas I
TEP130: Arquitectura, Patrimonio y Sostenibilidad: Acústica, Iluminación, Óptica y Energía
dc.subject.none.fl_str_mv Andalusia
Bottom-up model
Clustering
Energy model
Open data
Residential energy consumption
topic Andalusia
Bottom-up model
Clustering
Energy model
Open data
Residential energy consumption
description The energy budget of households, linked to residential energy consumption (REC), serves as a critical indicator of quality of life and economy trends. Despite the lack of widely available accurate statistics at regional or smaller scales, they are of crucial interest for a better understanding of the features influencing REC and its impact on energy poverty, wellbeing, and the climate crisis. This research aims to present a new information model for predictive parameters and REC forecasting through an innovative use of available open data. Geoprocessing, data mining, and machine learning clustering algorithms were applied to open datasets of location, population, and residential building stock parameters highly correlated with their REC, on the ensemble of 785 municipalities of Andalusia, Spain. The model identified 65 clusters of towns sharing the same potential REC, with 73% of the population concentrated in 10 of these. The resulting data-driven bottom-up model of provincial REC had a mean absolute error of only 0.63%. Furthermore, it provided the territorial distribution, with local resolution, of the identified clusters of cities with similar characteristics. This methodology, with a flexible regional- to city-scale analysis, provides knowledge generation that offers numerous practical applications for energy policy planning. Its future implementation would assist stakeholders and policymakers in enhancing the performance and decarbonization of the residential building stock.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/162499
https://doi.org/10.3390/buildings14082335
url https://hdl.handle.net/11441/162499
https://doi.org/10.3390/buildings14082335
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Buildings, 14(8) (2335).
https://www.mdpi.com/2075-5309/14/8/2335
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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