Indoor Air Quality Monitoring for Enhanced Healthy Buildings

Marques, Gonçalo; Pitarma, Rui

doi:10.5772/intechopen.81478

Cited by 18 publications

(10 citation statements)

References 36 publications

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“…But to get the result of the system prediction is so hard to get that the traditional method concludes the controller task's formation and the solution's prediction. In this way, the correct output can be instead of a forecast output for determining an NN's managed weight [31][32][33][34].…”

Section: Configuration Of Neural Network and Pid Based Controlmentioning

confidence: 99%

Indoor Air Quality Control Using Backpropagated Neural Networks

Uskenbayeva¹,

Altayeva²,

Gusmanova³

et al. 2022

Computers, Materials &Amp; Continua

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Providing comfortable indoor air quality control in residential construction is an exceedingly important issue. This is due to the structure of the fast response controller of air quality. The presented work shows the breakdown and creation of a mathematical model for an interactive, nonlinear system for the required comfortable air quality. Furthermore, the paper refers to designing traditional proportional integral derivative regulators and proportional, integral, derivative regulators with independent parameters based on a backpropagation neural network. In the end, we perform the experimental outputs of a suggested backpropagation neural network-based proportional, integral, derivative controller and analyze model results by applying the proposed system. The obtained results demonstrated that the proposed controller can provide the required level of clean air in the room. The proposed developed model takes into consideration international Heating, Refrigerating, and air conditioning standards as ASHRAE AND ISO. Based on the findings, we concluded that it is possible to implement a proposed system in homes and offer equivalent indoor air quality with continuous mechanical ventilation without a profuse amount of energy.

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Section: Configuration Of Neural Network and Pid Based Controlmentioning

confidence: 99%

Indoor Air Quality Control Using Backpropagated Neural Networks

Uskenbayeva¹,

Altayeva²,

Gusmanova³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…However, due to the advancement in technology, these poor and cheap methods of heating are now rapidly being replaced by electric powered heating devices. Considering the significance of indoor air quality there are several studies and platform developed by researchers to combat the poor indoor air quality and a review of several such systems including their architectures is described in References [13][14][15]. In Reference [16], a review of the several factors responsible for NO 2 emission and it's negative impacts on human respiration have been discussed.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

et al. 2021

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Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

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“…When pupils are involved in the IEQ evaluation in schools, a set of precautions related to building characteristics and individuals are required [8], [9]. The use of Internet of Thing-based (IoT) smart devices for monitoring the environmental variables and managing the indoor conditions is widely used for IEQ analysis [10], [11]. In this article, the IoT-based device nEMoS is used for the evaluation of the IEQ in two classrooms in an educational building in Tirana, Albania.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Indoor Environmental Quality in schools by combining survey and modelling: a case study in Albania

et al. 2021

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The article describes the outcomes of the monitoring campaign carried out in an educational building nearby Tirana, capital of Albania. Performed under the bilateral agreement between the National Research Council of Italy and the Ministry of Education and Sport of the Republic of Albania, the study proposes a combined approach for the assessment of the IEQ, not limiting only in the collection of objective data through the environmental monitoring of indoor variables but it also includes the subjective perception of indoor environment by the students through the compilation of a specific questionnaire. The classrooms were also modelled using a parametric model combining the functionalities of Honeybee plugin with OpenStudio and Radiance. The results are illustrated by maps of the indoor microclimate and illuminance with spatial resolution of 0.5 meters and one-hour temporal resolution step. Aggregated index and environmental data derived from models are compared to those monitored with nEMoS. This hybrid method overcomes the drawback caused by a low resolution of the monitoring steps and allows the comparison of the objective and subjective perception of the considered environmental factors, as well as the identification of discrepancies in terms of objective evidence and subjective perception related to the considered environmental factors.

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Indoor Air Quality Monitoring for Enhanced Healthy Buildings

Cited by 18 publications

References 36 publications

Indoor Air Quality Control Using Backpropagated Neural Networks

Indoor Air Quality Control Using Backpropagated Neural Networks

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

Assessment of Indoor Environmental Quality in schools by combining survey and modelling: a case study in Albania

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