Anomaly detection

Purpose

Anomaly detection helps improve the reliability of energy consumption and grid data by identifying atypical peaks, faulty measurements, and irregular load patterns. Its purpose is to filter out anomalies so that forecasts and demand profiles become more accurate and operational decisions can be based on cleaner data.

Primary users

The primary user is not specified. The agent is associated with Energy Trading and Portfolio Management contexts and is owned by Enexis and ProRail.

Where it fits (process/stage/trigger)

This agent fits into energy data preparation and forecasting workflows where historical consumption data and contextual datasets are reviewed before demand profiles, transport capacity planning, or risk assessments are produced.

Key capabilities / workflow

The agent analyzes historical consumption data and contextual datasets, including freely available KNMI weather data, to detect atypical peaks, faulty measurements, and irregular load patterns. Detected anomalies are filtered so downstream forecasting and capacity alignment processes can rely on more accurate data.

Inputs

Typical inputs include historical consumption data and various context datasets, including freely available weather data from KNMI. No additional input requirements were specified.

Outputs / Deliverables

Outputs are not fully specified. Based on the provided use case, the agent delivers anomaly detection results and filtered data that support more accurate forecasts, improved demand profiles, and better alignment of transport capacity with actual needs.

Value

The agent improves data reliability for energy consumption and grid analysis, helping reduce operational and financial risks. By filtering anomalous data, it supports more accurate forecasting, better demand profiling, and improved transport capacity planning.