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Multi-Sensor Platform for Predictive Air Quality Monitoring
Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO [Formula: see text]) is one of the pollutants that most affects people’s health. An automatic system able to accurately forecast CO [Formula: see text] concentration can prevent a sudden ris...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255563/ https://www.ncbi.nlm.nih.gov/pubmed/37299868 http://dx.doi.org/10.3390/s23115139 |
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author | Rescio, Gabriele Manni, Andrea Caroppo, Andrea Carluccio, Anna Maria Siciliano, Pietro Leone, Alessandro |
author_facet | Rescio, Gabriele Manni, Andrea Caroppo, Andrea Carluccio, Anna Maria Siciliano, Pietro Leone, Alessandro |
author_sort | Rescio, Gabriele |
collection | PubMed |
description | Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO [Formula: see text]) is one of the pollutants that most affects people’s health. An automatic system able to accurately forecast CO [Formula: see text] concentration can prevent a sudden rise in CO [Formula: see text] levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people’s comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware–software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO [Formula: see text] trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants’ physical activity, temperature, humidity and CO [Formula: see text] in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days. |
format | Online Article Text |
id | pubmed-10255563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102555632023-06-10 Multi-Sensor Platform for Predictive Air Quality Monitoring Rescio, Gabriele Manni, Andrea Caroppo, Andrea Carluccio, Anna Maria Siciliano, Pietro Leone, Alessandro Sensors (Basel) Article Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO [Formula: see text]) is one of the pollutants that most affects people’s health. An automatic system able to accurately forecast CO [Formula: see text] concentration can prevent a sudden rise in CO [Formula: see text] levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people’s comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware–software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO [Formula: see text] trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants’ physical activity, temperature, humidity and CO [Formula: see text] in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days. MDPI 2023-05-28 /pmc/articles/PMC10255563/ /pubmed/37299868 http://dx.doi.org/10.3390/s23115139 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rescio, Gabriele Manni, Andrea Caroppo, Andrea Carluccio, Anna Maria Siciliano, Pietro Leone, Alessandro Multi-Sensor Platform for Predictive Air Quality Monitoring |
title | Multi-Sensor Platform for Predictive Air Quality Monitoring |
title_full | Multi-Sensor Platform for Predictive Air Quality Monitoring |
title_fullStr | Multi-Sensor Platform for Predictive Air Quality Monitoring |
title_full_unstemmed | Multi-Sensor Platform for Predictive Air Quality Monitoring |
title_short | Multi-Sensor Platform for Predictive Air Quality Monitoring |
title_sort | multi-sensor platform for predictive air quality monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255563/ https://www.ncbi.nlm.nih.gov/pubmed/37299868 http://dx.doi.org/10.3390/s23115139 |
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