Load Pattern Analyzer
Identifies and analyzes consumption patterns to optimize capacity and detect anomalies.
Discover our Solution
The Load Pattern Analyzer leverages advanced analytics to further process the data collected, validated and structured by the Energy Data Manager module. By building on this trusted and consistent data layer, it enhances the value of metering and IoT information with deeper contextualisation and refined data modelling.
Using machine learning, the module identifies consumption trends, behavioural patterns and emerging network insights across customer groups and grid areas.
It works seamlessly with other modules such as Demand Forecaster and Advanced Smart Grid Analytics, ensuring consistent and reliable insights across all datasets.
Delivered as Software as a Service (SaaS), the solution offers scalability, continuous updates and secure integration with utility systems for future-proof grid intelligence.
Covered use cases
Automated load segmentation
Meters and feeders are grouped by similar load profiles to identify customer types and usage behaviour. The clustering adapts dynamically as consumption patterns evolve, providing a real-time view of grid diversity.
Intelligent EV and solar detection
New patterns caused by EV charging, heat pumps or rooftop solar panels are detected automatically without manual setup. The system continuously learns from data, revealing where decentralised generation or new demand emerges.
Advanced feeder and transformer analysis
Each network segment is monitored for overloads, imbalances and capacity constraints. Pattern-based analytics replace static threshold rules, providing earlier and more reliable insight.
Self-learning anomaly detection
Abnormal usage, data loss and malfunctioning meters are detected through self-learning algorithms. This enables targeted maintenance and fewer unnecessary field visits.
Cluster-based forecast enhancement
Cluster-level load profiles are used to refine demand forecasting models for greater precision and stability. The result is better short- and long-term planning for operations and procurement.
Tracking long-term demand trends
Evolving energy behaviours such as EV adoption or seasonal load shifts are continuously monitored. Trend insights help utilities plan network investments and adapt to changing consumption dynamics.