Client

Grid-India

Challenge

Grid-India's teams were managing weather data and power grid information through disconnected systems, with no unified view to support operational decisions. The core challenge wasn't just data access; it was interpretation. Without a way to correlate weather parameters with demand and generation patterns, operators had no reliable basis for anticipating disruptions or planning responses before they became incidents.

Solution

The architecture was built around a central data layer pulling weather and grid data simultaneously, surfaced on a single map-based interface operators could act on directly. The architectural decision Embedding AI models into the system allowed impact analysis to go beyond static reporting. Using historically available data, the models were trained to predict how specific weather conditions would affect power demand and generation, turning the platform from an information display into a genuine decision support tool.

Results

Development was structured in functional layers; map and grid visualization first, then weather integration, then the AI impact analysis module, giving the Grid-India team working visibility at each stage. The final system brought together Python-backed ML models for predictions, a Nest.js API layer managing data flow, and a Next.js frontend rendering grid and weather data in real time.

Key Achievements

• Improved Grid Operations Enhanced Data Visibility AI Driven Insights Streamlined Decision Making

Technologies Used

Deliverables