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

Date

2025-03-09

Director

Publisher

MDPI
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

  • AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136627NB-I00/ES/ recolecta
Impacto
OpenAlexGoogle Scholar
cited by count

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.

Description

Keywords

Air quality modeling, Air quality sensors, Forecasting, Indoor air quality, Machine learning, Pollutants

Department

Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika / Institute of Smart Cities - ISC

Faculty/School

Degree

Doctorate program

item.page.cita

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.

item.page.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.

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