Smart Green Building Platform: Enhancing Indoor Air Quality with AI and IoT

The primary objective of this project is to develop a spatially intelligent smart green building platform that enhances indoor air quality (IAQ) monitoring and management through AI-driven analytics and IoT devices. This platform aims to revolutionize smart building management by leveraging real-time sensor data, AI-based anomaly detection, and multi-modal retrieval-augmented generation (RAG) to improve air quality and environmental conditions in commercial and residential buildings. Key focus areas include: Integration with External Data Sources : Connect with public air quality datasets, IoT sensor networks, and corporate environmental reports to enhance IAQ analytics. Localized LLMs with Multi-Modal RAG : Use retrieval-augmented generation (RAG) techniques for analyzing sensor data and generating actionable insights on air quality trends. AI-Agent Solutions for Abnormality Detection : Deploy AI-driven agents to identify air quality anomalies and provide automated handling solutions. Spatial Intelligence for IAQ Optimization : Utilize location-based data to optimize ventilation, HVAC control, and pollutant mitigation strategies . Automation of IAQ Monitoring and Reporting : Implement AI-driven analytics to replace manual assessments with real-time, data-driven decision-making. Enhanced User Interface & Experience : Develop an intelligent dashboard that provides interactive visualizations and alerts for indoor air quality monitoring. Advanced AI-Driven Report Generation : Utilize IoT sensor data and generative AI to create detailed reports on air quality trends, pollutant sources, and mitigation strategies.