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
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DOI:
10.17148/IJARCCE.2025.1411148
[1] Shamail Rasha, Sonia Y, Vishakha S D, Suhana D, Dr. Manjula B B, "AI BASED SMART INDOOR AIR QUALITY PREDICTION AND MONITORING SYSTEM," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.1411148
