AI-Powered Energy Forecasting and Building Optimization

✦ Inspiration : Buildings waste massive amounts of energy due to inefficient scheduling and unpredictable renewable supply. We wanted to design a solution that not only consumes renewable energy but also optimizes usage in real time to cut costs and emissions.

✦ What it does : Our system uses AI-driven energy optimization to forecast demand, detect anomalies, and dynamically balance renewable and grid energy. It helps buildings save money, reduce carbon footprints, and operate more sustainably—all without sacrificing comfort or performance.

✦ How we built it : • Designed a privacy-first architecture to protect building data • Integrated AI foundation models for demand forecasting and anomaly detection • Enabled real-time, edge-based decision-making for instant response • Developed a plug-and-play API layer for seamless integration with existing building systems

✦ Challenges we ran into : • Handling data scarcity while ensuring model accuracy • Scaling the system to work for both small homes and large enterprises • Managing the fluctuations of renewable supply in real-time optimization

✦ Accomplishments that we are proud of : • Built a working prototype that demonstrated 15–20% cost savings in simulations • Designed an architecture that is scalable, privacy-conscious, and flexible • Successfully applied AI foundation models beyond text and vision to real-world sustainability challenges

✦ What we learned : We discovered the potential of combining AI with sustainability, learned how to make zero-shot models effective for real-time systems, and realized how crucial scalability and privacy are for real-world adoption.

✦ What’s next for our project : We plan to expand from buildings to entire smart cities and microgrids, making renewable-first energy management the global default. Our goal is to scale this worldwide, so every building can become a smart, sustainable building.