Work

A Comparative Study of CO2 Forecasting Strategies in School Classrooms: A Step Toward Improving Indoor Air Quality

Year: 2025

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s25072173

Authors: Peio Garcia-Pinilla, Aranzazu Jurio, Daniel Paternain

Institutions: Universidad Pública de Navarra (UPNA), Instituto de Smart Cities

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $3,137