AI BASED SMART INDOOR AIR QUALITY PREDICTION AND MONITORING SYSTEM

Abstract: Indoor Air Quality (IAQ) greatly affects health, comfort, and sustainability in modern spaces. This project introduces an AI-powered Indoor Air Wellness System that uses Kalman Filter and LSTM algorithms to predict and monitor air quality in real time. Sensors for CO₂, CO, NO₂, temperature, humidity, and light work with an ESP32 microcontroller to process and share data via LCD, ThingSpeak, and Telegram. A solar-powered fan system replaces traditional HVAC, promoting energy efficiency and eco-friendly air circulation. By combining AI, IoT, and renewable energy, the system offers a smart and sustainable way to maintain healthy indoor environments.

Keywords: Indoor Air Quality (IAQ), AI, Kalman Filter, LSTM, ESP32, IoT, Smart Monitoring, Renewable Energy, Energy Efficiency, Sustainability