Internet-of-Things-Based CO2 Monitoring and Forecasting System for Indoor Air Quality Management
Marya J. Marquez-Zepeda, Ildeberto Santos-Ruiz, Esvan-Jesús Pérez-Pérez, Adrián Navarro-Díaz, Jorge-Alejandro Delgado-Aguiñaga
Source 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.
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