Business Problem

With increasing customer base and subsequent demand for energy, energy providers were facing significant challenges in their ability to forecast demand on a real-time basis.

Need for a built in Advanced AI and machine learning platform which would provide insights in a timely, automated manner

  • Lack of systems to capture meter- level data, store and process it to derive business insights
  • Create a forecasting model which would be powerful enough to predict the unseen patterns

The Solutions

Flexible Data Lake

Architected the data lake with dynamic configuration to quickly onboard multiple customers.

Enhanced Operational Efficiency

Implemented the following modules: Data Platform and Data Lake, Load Forecasting, Risk Management and Load Scheduling.

Data-driven Insights

Developed a risk management tool which allows REPs (Retail Energy Providers) to make decisions based on the usage patterns.

Efficient Load Scheduling

Built a load scheduling tool, which allows to schedule bids and offers needed by REPs, Utilities, Scheduling Coordinators and Traders.

Accurate Forecasting Models

Tested different forecasting models with multiple validations built with hybrid models.

Value Delivered

The grid is fully autonomous and reliable.

Ability to derive insights in a real time manner to meet fluctuating demand.

Created a structured, scalable data lake model which could scale up for future requirements.