A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>

COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO<sub>2</sub>) represents one of the pollutants that most affects environmental health....

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Autores principales:	Giacomo Segala, Roberto Doriguzzi-Corin, Claudio Peroni, Tommaso Gazzini, Domenico Siracusa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	indoor air quality carbon dioxide air pollution artificial intelligence deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/9b22b0dbbb8e49e79e056b6e46d28a0e
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spelling	oai:doaj.org-article:9b22b0dbbb8e49e79e056b6e46d28a0e2021-11-25T16:37:50ZA Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>10.3390/app1122107712076-3417https://doaj.org/article/9b22b0dbbb8e49e79e056b6e46d28a0e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10771https://doaj.org/toc/2076-3417COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO<sub>2</sub>) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO<sub>2</sub> plays a central role in taking preventive measures to keep CO<sub>2</sub> level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO<sub>2</sub> using a limited window of recent environmental data (i.e., temperature; humidity; CO<sub>2</sub> of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance.Giacomo SegalaRoberto Doriguzzi-CorinClaudio PeroniTommaso GazziniDomenico SiracusaMDPI AGarticleindoor air qualitycarbon dioxideair pollutionartificial intelligencedeep learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10771, p 10771 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	indoor air quality carbon dioxide air pollution artificial intelligence deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	indoor air quality carbon dioxide air pollution artificial intelligence deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Giacomo Segala Roberto Doriguzzi-Corin Claudio Peroni Tommaso Gazzini Domenico Siracusa A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
description	COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO<sub>2</sub>) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO<sub>2</sub> plays a central role in taking preventive measures to keep CO<sub>2</sub> level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO<sub>2</sub> using a limited window of recent environmental data (i.e., temperature; humidity; CO<sub>2</sub> of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance.
format	article
author	Giacomo Segala Roberto Doriguzzi-Corin Claudio Peroni Tommaso Gazzini Domenico Siracusa
author_facet	Giacomo Segala Roberto Doriguzzi-Corin Claudio Peroni Tommaso Gazzini Domenico Siracusa
author_sort	Giacomo Segala
title	A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
title_short	A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
title_full	A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
title_fullStr	A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
title_full_unstemmed	A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>
title_sort	practical and adaptive approach to predicting indoor co<sub>2</sub>
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9b22b0dbbb8e49e79e056b6e46d28a0e
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A Practical and Adaptive Approach to Predicting Indoor CO<sub>2</sub>

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