Author(s): Brito, Thadeu ; Lima, José ; Biondo, Elias Junior ; Nakano, Alberto Yoshiro ; Pereira, Ana I.
Date: 2023
Persistent ID: http://hdl.handle.net/10198/29102
Origin: Biblioteca Digital da UPB
Project/scholarship: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB/05757/2020/PT; info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP/05757/2020/PT;
Subject(s): Internet of things; Wireless sensor network; Indoor air quality; Artificial neural network
Indoor Air Quality (IAQ) pertains to the air quality within a specific space and is directly linked to the well-being and comfort of its occupants. In line with this objective, this research presents a real-time system dedicated to monitoring and predicting IAQ, encompassing both thermal comfort and gas concentration. The system initiates with a data acquisition, wherein a set of sensors captures environmental parameters and transmits this data for storage in a database. The measured parameters are analyzed by a neural network algorithm that predicts anomalies based on historical data. The neural network model generated predictions from 75.9% to 98.1% (depending on the parameter) of precision during regular situations. After that, a test with smoke in the same place was done to validate the model, and the results showed it could detect anomalies. Finally, prediction data are stored in a new database and displayed on a dashboard for monitoring in real-time measured and prediction data.
