LSTM-Based IoT-Enabled CO₂ Steady-State Forecasting for Indoor Air Quality Monitoring

Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO${}_2$) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO${}_2$ data for the purpose of predicting future CO${}_2$ concentrations. Based on the predicted CO${}_2$ concentration, our system can compute CO${}_2$ steady state in advance with an error margin of 5.5%.