Inspiration
India is facing rapid urbanization and rising electricity demand. Yet, millions still experience blackouts, and renewable energy often goes underutilized.
We wanted to build an AI system that not only forecasts energy use but also acts intelligently to reduce waste and enable a greener future.
What it does
UjjwalAI is an Agentic AI system for smart energy management. It:
- Forecasts energy demand using IBM Time Series Foundation Models (TSFM)
- Takes autonomous actions with IBM Agent Development Kit (ADK) (e.g., load shifting, anomaly detection)
- Provides multilingual AI assistance with IBM Granite models to help users understand and save energy
Example: If peak demand is predicted tomorrow, the AI reschedules appliances, suggests solar usage, and alerts if abnormal power theft is detected.
How we built it
- Collected synthetic energy datasets
- Trained demand forecasting with IBM TSFM
- Integrated ADK agents for decision-making
- Built a simple dashboard + chatbot powered by Granite for user interaction
Challenges we ran into
- Understanding agentic AI and connecting it with real-world energy use cases
- Optimizing time series forecasting for small datasets
- Making the system multilingual and accessible for all users
Accomplishments that we’re proud of
- Building a working prototype that doesn’t just predict but also acts autonomously
- Combining IBM’s AI stack (TSFM + ADK + Granite) into a real-world use case
- Creating a solution scalable from homes → industries → smart cities
What we learned
- Hands-on experience with IBM foundation models and agentic frameworks
- The importance of designing AI that is not just analytical but also action-oriented
- How to bridge sustainability, automation, and user accessibility in one project
What’s next
- Deploy UjjwalAI with real smart meters and IoT devices
- Expand to large-scale renewable integration (solar farms, microgrids)
- Partner with government/enterprises to support India’s green energy transition
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