A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality
| dc.contributor.author | Garcia-Pinilla, Peio | |
| dc.contributor.author | Jurío Munárriz, Aránzazu | |
| dc.contributor.author | Paternain Dallo, Daniel | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.funder | Gobierno de Navarra / Nafarroako Gobernua | |
| dc.date.accessioned | 2025-06-18T08:24:59Z | |
| dc.date.available | 2025-06-18T08:24:59Z | |
| dc.date.issued | 2025-03-09 | |
| dc.date.updated | 2025-06-18T08:18:30Z | |
| dc.description.abstract | 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. | en |
| dc.description.sponsorship | 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'. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Garcia-Pinilla, P., Jurio, A., Paternain, D. (2025). A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality. Sensors, 25(7), 1-18. https://doi.org/10.3390/s25072173. | |
| dc.identifier.doi | 10.3390/s25072173 | |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/54238 | |
| dc.language.iso | eng | |
| dc.publisher | MDPI | |
| dc.relation.ispartof | Sensors (2025), vol. 25, núm. 7 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136627NB-I00/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.3390/s25072173 | |
| dc.rights | © 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. | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Air quality modeling | en |
| dc.subject | Air quality sensors | en |
| dc.subject | Forecasting | en |
| dc.subject | Indoor air quality | en |
| dc.subject | Machine learning | en |
| dc.subject | Pollutants | en |
| dc.title | A comparative study of CO2 forecasting strategies in school classrooms: a step toward improving indoor air quality | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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