Case Study
The Challenge
Accurately predicting market clearing prices for the Day Ahead Market (DAM) and Real Time Market (RTM) is critical for optimizing bidding strategies. The challenge lay in tackling the market’s dynamic, non-linear behavior and delivering real-time, reliable forecasts.
The Solution
Our team designed an innovative solution using a robust tech stack:
Python
For data analysis and model deployment.
PyTorch & TensorFlow
Built accurate, deep learning models.
Django
Developed a scalable web application.
Celery
Enabled real-time, asynchronous predictions.
AWS (EC2)
Powered scalable cloud-based operations.
Docker
Streamlined deployment and scalability.
Results and Impact
The GMR Project delivered highly accurate forecasts with a 20% improvement in prediction accuracy, empowering energy providers with:
- Optimized bidding strategies in both DAM and RTM.
- A scalable, future-ready system to handle evolving energy market needs.
Future Directions
Building on its success, the project aims to integrate renewable energy factors and expand predictive capabilities to new markets.
Conclusion
The GMR Project set a new benchmark in energy market forecasting, proving that advanced machine learning and scalable technology can drive smarter, more profitable decision-making.