Anomaly Detection
Anomaly detection is the use of statistical or machine learning methods to automatically identify energy consumption patterns that deviate significantly from expected or historical norms.
Definition
Anomaly detection refers to methods — ranging from simple threshold alerts to machine learning models — used to identify energy consumption that falls outside expected patterns. In an energy context, this typically means flagging usage that's significantly higher or lower than what a baseline model, historical pattern, or comparable site would predict.
More advanced anomaly detection can catch subtler issues than simple threshold alerts, such as a piece of equipment gradually drawing more power over weeks (indicating developing mechanical wear) rather than only flagging sudden spikes.
Why It Matters
Faults, equipment left running, and gradual efficiency degradation often go unnoticed for weeks without automated anomaly detection — by the time the cost shows up clearly on a bill, the waste has often been accumulating for a long time already.
How ecolyptus Helps
ecolyptus's Forecasting module models an expected consumption range for each meter and flags any deviation in near real time, so anomalies are surfaced within hours or days rather than discovered at the next billing cycle.