Energy Sector: Smart Grid Decision Support System
Empowering grid operators with AI-driven load forecasting.
The Challenge
Renewables are unpredictable. GreenGrid struggled to balance supply (wind/solar) with demand. Over-supply led to negative pricing; under-supply required expensive spot-market purchases. Operators needed a 'Decision Support System' to predict generation vs. load and suggest trading actions in real-time.
The Solution
GTEMAS provided a high-performance engineering team. We built a streaming data pipeline ingesting weather data, turbine sensors, and market prices. The Brain: An AI model forecasts generation 24 hours ahead. The Interface: An Angular dashboard visualizes the 'Energy Gap' and recommends trading actions (e.g., 'Sell 50MW at 2 PM').
Architectural Strategy
Event-driven architecture using Kafka and Akka for high-concurrency data processing. Scalable on Kubernetes.
Impact & Achievements
The platform became the heart of the trading floor. It allowed GreenGrid to maximize the value of their renewable assets and stabilize the grid.
Accuracy of load prediction
Improved arbitrage margins
From hours to seconds
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