A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality

This paper comprehensively investigates the performance of various strategies for predicting CO2 levels in school classrooms over different time horizons by using data collected through IoT devices. We gathered Indoor Air Quality (IAQ) data from fifteen schools in Navarra, Spain between 10 January a...

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Detalhes bibliográficos
Autores: Garcia-Pinilla, Peio, Jurío Munárriz, Aránzazu, Paternain Dallo, Daniel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/54238
Acesso em linha:https://hdl.handle.net/2454/54238
Access Level:acceso abierto
Palavra-chave:Air quality modeling
Air quality sensors
Forecasting
Indoor air quality
Machine learning
Pollutants
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spelling A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air qualityGarcia-Pinilla, PeioJurío Munárriz, AránzazuPaternain Dallo, DanielAir quality modelingAir quality sensorsForecastingIndoor air qualityMachine learningPollutantsThis paper comprehensively investigates the performance of various strategies for predicting CO2 levels in school classrooms over different time horizons by using data collected through IoT devices. We gathered Indoor Air Quality (IAQ) data from fifteen schools in Navarra, Spain between 10 January and 3 April 2022, with measurements taken at 10-min intervals. Three prediction strategies divided into seven models were trained on the data and compared using statistical tests. The study confirms that simple methodologies are effective for short-term predictions, while Machine Learning (ML)-based models perform better over longer prediction horizons. Furthermore, this study demonstrates the feasibility of using low-cost devices combined with ML models for forecasting, which can help to improve IAQ in sensitive environments such as schools.A.J. and D.P. were partially supported by the Spanish Ministry of Science and Innovation through the project PID2022-136627NB-I00 (MCIN/AEI/10.13039/501100011033/FEDER, UE). P.G.-P. was supported by the Gobernment of Navarra under 'Doctorados Industriales 2021'.MDPIEstadística, Informática y MatemáticasEstatistika, Informatika eta MatematikaInstitute of Smart Cities - ISCGobierno de Navarra / Nafarroako Gobernua2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2454/54238reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136627NB-I00© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/542382026-06-17T12:41:47Z
dc.title.none.fl_str_mv A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
title A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
spellingShingle A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
Garcia-Pinilla, Peio
Air quality modeling
Air quality sensors
Forecasting
Indoor air quality
Machine learning
Pollutants
title_short A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
title_full A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
title_fullStr A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
title_full_unstemmed A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
title_sort A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
dc.creator.none.fl_str_mv Garcia-Pinilla, Peio
Jurío Munárriz, Aránzazu
Paternain Dallo, Daniel
author Garcia-Pinilla, Peio
author_facet Garcia-Pinilla, Peio
Jurío Munárriz, Aránzazu
Paternain Dallo, Daniel
author_role author
author2 Jurío Munárriz, Aránzazu
Paternain Dallo, Daniel
author2_role author
author
dc.contributor.none.fl_str_mv Estadística, Informática y Matemáticas
Estatistika, Informatika eta Matematika
Institute of Smart Cities - ISC
Gobierno de Navarra / Nafarroako Gobernua
dc.subject.none.fl_str_mv Air quality modeling
Air quality sensors
Forecasting
Indoor air quality
Machine learning
Pollutants
topic Air quality modeling
Air quality sensors
Forecasting
Indoor air quality
Machine learning
Pollutants
description This paper comprehensively investigates the performance of various strategies for predicting CO2 levels in school classrooms over different time horizons by using data collected through IoT devices. We gathered Indoor Air Quality (IAQ) data from fifteen schools in Navarra, Spain between 10 January and 3 April 2022, with measurements taken at 10-min intervals. Three prediction strategies divided into seven models were trained on the data and compared using statistical tests. The study confirms that simple methodologies are effective for short-term predictions, while Machine Learning (ML)-based models perform better over longer prediction horizons. Furthermore, this study demonstrates the feasibility of using low-cost devices combined with ML models for forecasting, which can help to improve IAQ in sensitive environments such as schools.
publishDate 2025
dc.date.none.fl_str_mv 2025
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/2454/54238
url https://hdl.handle.net/2454/54238
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136627NB-I00
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.198674

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