Operations & Compliance
Energy consumption anomaly detection
Analyse energy usage data across sites and equipment to flag consumption anomalies that may indicate equipment failure, inefficiency, or leakage. Supports sustainability reporting and cost management.
5–10%
reduction in energy cost through anomaly resolution
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Requires granular, time-series energy data at sub-meter or equipment level. Whole-building monthly meter readings do not have enough resolution for anomaly detection.
Anomalies need context — a spike during a heat wave is expected, a spike on a quiet Sunday is suspicious. The analysis must account for operational calendars, occupancy, and seasonal patterns.
Specialist IoT and building management systems often have energy monitoring built in. Evaluate whether existing systems already surface this insight before building a custom solution.
The financial case must account for the cost of smart metering infrastructure if it doesn't already exist. For smaller sites, the capital investment in sub-metering may exceed the energy savings from anomaly detection. Model this before committing.
Experiment starter: For your highest-energy site, download 12 months of available meter data at the finest granularity available. Prompt an LLM to identify weeks where consumption was significantly above trend, then cross-reference with the operational calendar. If the AI identifies anomalies that the facilities team can explain retrospectively (a fault, a door left open, equipment left running), the detection approach is working.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in Operations & Compliance
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