Real-Time Indoor Air Quality Analysis using Recurrent Neural Networks: A Case Study of Environmental Variables

Carlos Alejandro Reyes Pérez; Miguel Enrique Iglesias Martínez; Jose Guerra Carmenate; Humberto Michinel Álvarez; Eduardo Balvis; Fernando Giménez Palomares; Pedro Fernández de Cordoba

doi:10.20944/preprints202310.1163.v1

A peer-reviewed article of this Preprint also exists.

Reyes Pérez, C.A.; Iglesias Martínez, M.E.; Guerra-Carmenate, J.; Michinel Álvarez, H.; Balvis, E.; Giménez Palomares, F.; Fernández de Córdoba, P. Indoor Air Quality Analysis Using Recurrent Neural Networks: A Case Study of Environmental Variables. Mathematics 2023, 11, 4872. Reyes Pérez, C.A.; Iglesias Martínez, M.E.; Guerra-Carmenate, J.; Michinel Álvarez, H.; Balvis, E.; Giménez Palomares, F.; Fernández de Córdoba, P. Indoor Air Quality Analysis Using Recurrent Neural Networks: A Case Study of Environmental Variables. Mathematics 2023, 11, 4872.

Abstract

In the pursuit of energy efficiency and reduced environmental impact, adequate ventilation in enclosed spaces is essential. This study presents a hybrid neural network model designed for real-time monitoring and prediction of environmental variables. The system comprises two phases: An IoT hardware-software platform for data acquisition and decision-making, and a hybrid model combining short-term memory and convolutional recurrent structures. The results are promising and hold potential for integration into parallel processing AI architectures.

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Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Real-Time Indoor Air Quality Analysis using Recurrent Neural Networks: A Case Study of Environmental Variables

Abstract

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