Garcia-Pinilla, PeioJurío Munárriz, AránzazuPaternain Dallo, Daniel2025-06-182025-06-182025-03-09Garcia-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/s25072173.1424-822010.3390/s25072173https://academica-e.unavarra.es/handle/2454/54238This 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.application/pdfeng© 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.Air quality modelingAir quality sensorsForecastingIndoor air qualityMachine learningPollutantsinfo:eu-repo/semantics/article2025-06-18info:eu-repo/semantics/openAccess