GREEN NEXA

Many institutions and public facilities struggle to monitor energy consumption, water usage, environmental conditions, and resource efficiency in one place. Without centralized monitoring, it becomes difficult to identify unusual patterns, detect inefficiencies, and take timely action. We set out to build GreenNexa, an AI-powered sustainability monitoring dashboard that helps institutions make smarter, data-driven decisions.

We developed GreenNexa as a web-based sustainability intelligence platform combining frontend development, backend APIs, database management, and IoT data integration. The platform is designed to monitor facility data, visualize trends, identify anomalies, and provide actionable insights. Our technology stack includes React for the frontend, Python with FastAPI for backend services, Supabase for database management, and ESP32-based sensors for IoT data collection. We also explored AI/ML-based anomaly detection and forecasting to support smarter facility management.

We created a working prototype of GreenNexa that brings sustainability monitoring and facility intelligence into a unified dashboard. The project demonstrates how sensor-style or synthetic data can support visualization, monitoring, and data-driven decision-making. It provides a foundation for identifying resource inefficiencies and improving sustainability management. The project was submitted to a hackathon, and the results are awaited.

Working on GreenNexa helped me understand how frontend development, backend APIs, databases, and IoT systems work together in a real-world project. I gained practical experience in integrating different technologies, debugging issues, and collaborating with a team. I learned that reliable data flow and thorough testing are essential for building useful technology solutions. In the future, I would improve real-sensor integration, validate AI/ML predictions with more data, and strengthen the platform's reliability and scalability.