Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

Abstract

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

Relearning procedure to adapt pollutant prediction neural model: Choice of relearning algorithm

“…They develop an artificial neural network model (ANN) able to link different surveyed pollutants, including CO2, pm, VOC, airborne bacteria and fungi, to an occupant symptom metric. Another approach is to use the pollutants levels collected by sensors to classify the sources influencing IAQ like fragrance presence, foods and beverages, human activities as window opening, by using ANN classifiers [23][24].…”

“…All the weights of this MLP has been learned by using a classical learning algorithm (here a robust version of the Levenberg-Marquardt algorithm [24]). When the control chart described at part IV indicates that a relearning is needed, these weights must be updated.…”

Smart Indoor Air Quality Monitoring System

“…Similarly, classification of pollutants is done based on five parameters-ambient air, foods and beverages, chemical presence, and human activity in [10]. Artificial Neural Network is used for classification in [48]. Authors have also discussed monitoring of various pollutants such as Carbon Dioxide (CO 2 ), Carbon Monoxide (CO), Nitrogen Dioxide (NO 2 ), Ozone (O3), Particulate Matter (PM 10), Volatile Organic Compound (VOCs) and Oxygen (O 2 ).…”

Managing In-home Environments through Sensing, Annotating, and Visualizing Air Quality Data

“…A significant amount of work explores the technical aspects of air quality sensing from various perspectives and application areas: commercial monitoring and HVAC control [7,25,27], system infrastructure and platform development [8,26,28,47], mobile sensing [13,21,26], personal exposure monitoring [26,42], and source detection and classification [16,49]. These studies primarily focus on a prototype's technical contributions or proof-of-concept systems architecture [7,8,21,24,25,28,47,49]. Early work by Postolache et al [47] proposes a multi-monitor air quality sensing system, with significant work on modeling, calibrating, and processing sensor data to ensure an accurate system.…”