Automating alarm and event processing to correlate network events with consumption and production data. Applying VEE (Validation, Estimation and Editing) to ensure accurate and reliable data. Defining flexible Time-of-Use (TOU) structures through billing determinant and time-series aggregation. Enabling advanced analytics.

Creating consumer clusters based on energy usage patterns to enable customer segmentation, business insights, or improved forecasting. Automating re-clustering schedules and tracking consumption or production trends over time by aggregating the underlying meters.

Using machine learning models on load curves and weather forecasts to predict electricity demand for individual or grouped meters, supporting both short-term and long-term forecasting horizons.

Using advanced analytics and machine learning to detect non-technical losses, verify grid topology, and identify irregular consumption patterns, helping utilities prevent energy theft, improve billing accuracy, and safeguard revenues.

Analyzing low-voltage grid data to monitor network performance, detect anomalies, and anticipate maintenance needs, enabling utilities to prevent faults, manage capacity, and ensure reliable, efficient grid operations.