Work

Internet-of-Things-Based CO2 Monitoring and Forecasting System for Indoor Air Quality Management

Year: 2025

Type: article

Abstract: This study presents a low-cost and scalable CO2 monitoring system that leverages NDIR sensors and a Long Short-Term Memory (LSTM) neural network to predict indoor CO2 concentrations over both short- and long-term horizons. The proposed system aims to anticipate air quality deterioration in shared spaces, enabling proactive ventilation strategies. Various LSTM configurations were evaluated, optimizing the number of layers, neurons per layer, and input delays to enhance forecasting accuracy. The optimal model consisted of two LSTM layers with 128 neurons each and a time window of 10 previous observations. This model achieved an RMSE of approximately 57 ppm for an 8 h forecast in a classroom setting. Experimental results demonstrate the reliability of the proposed approach for CO2 prediction and its potential impact on indoor air quality management.

Source: Mathematical and Computational Applications

DOI: 10.3390/mca30020036

Authors: Marya J. Marquez-Zepeda, Ildeberto Santos-Ruiz, Esvan-Jesús Pérez-Pérez, Adrián Navarro-Díaz, Jorge-Alejandro Delgado-Aguiñaga

Institutions: Tuxtla Gutierrez Institute of Technology, Tecnológico Nacional de México, Tecnológico de Monterrey, Universidad del Valle de México

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: $1,689