LSTM-Based IoT-Enabled CO2 Steady-State Forecasting for Indoor Air Quality Monitoring
نویسندگان
چکیده
Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead a series negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for variety reasons. Various monitoring systems are available on the market. However, since they expensive difficult obtain, not commonly employed general population. With advent Internet Things (IoT), Indoor Air Quality (IAQ) system has been simplified, number studies have conducted in order monitor IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost Artificial Intelligence (AI) provide recommendations. our proposed system, IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which visualised real user-friendly dashboard. Furthermore, AI technique referred Long Short-Term Memory (LSTM) is applied collected CO2 purpose predicting future concentrations. Based predicted concentration, compute steady state advance with error margin 5.5%.
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ژورنال
عنوان ژورنال: Electronics
سال: 2022
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12010107