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Energy Anomaly Detection Engine

Algorithm purpose​

This Compute-to-Data algorithm is aimed at the early detection of technical issues and critical behaviors in energy datasets. Its purpose is to identify situations that may indicate operational failures, technical incidents, or imminent risks, enabling proactive action before they become major problems.

It does not replace real-time monitoring systems. Instead, it serves as an analytical diagnostic on historical or quasi-historical data, generating clear, actionable signals without exposing sensitive information.

What the algorithm does (high-level overview)​

1. Detects extreme consumption spikes​

The algorithm analyzes consumption time series and detects:

  • consumption spikes significantly above typical behavior
  • isolated events that break normal trends
  • concentrated consumption during critical periods

These spikes may indicate equipment faults, operational errors, or unexpected usage.

2. Identifies periods of technical inactivity​

The algorithm identifies intervals where:

  • data is not received from certain supply points
  • sensors stop reporting values for abnormal durations
  • time gaps are inconsistent with expected operation

These situations are often linked to communication issues, sensor failures, or technical interruptions.

3. Detects unusual energy exports​

In systems with distributed generation, it analyzes:

  • atypical energy export events
  • unexpected surpluses during certain periods
  • injection patterns that do not match the usual profile

These signals may indicate configuration errors, metering issues, or conditions requiring technical review.

4. Produces secure, prioritized analytical alerts​

The output includes:

  • identification of anomalous events
  • classification by anomaly type
  • severity or recurrence indicators
  • temporal summaries of incidents

All information is presented in an aggregated and anonymized way, without exposing individual values or sensitive data.

How this algorithm supports the Compute-to-Data model​

1. Diagnostics without raw data extraction​

The analysis runs entirely inside the secure environment. Only derived events and alert metrics are exported.

2. Enables proactive and preventive maintenance​

Before critical failures occur, it is key to answer:

  • Where are recurrent incidents appearing?
  • Which events require immediate review?
  • Which assets show early signs of degradation?

This algorithm provides structured answers.

3. Reduces operating costs and risk​

By detecting issues early, it helps:

  • avoid severe breakdowns
  • reduce downtime
  • plan technical interventions more efficiently

Why this algorithm is valuable for the energy sector​

1. Key technical diagnostic tool​

It identifies current or potential problems quickly and data-driven, reducing reliance on constant inspections.

2. Foundation for preventive maintenance programs​

Detected anomalies help:

  • design maintenance schedules
  • prioritize critical assets
  • improve reliability of installations and non-intensive networks

3. Applicable across many energy contexts​

It is especially useful for:

  • energy communities
  • public installations
  • local and non-intensive networks
  • environments with distributed generation

4. Compatible with public and private sector use​

Its secure, aggregated approach makes it suitable for:

  • public administrations
  • infrastructure operators
  • energy managers
  • service companies

Summary​

The Energy Anomaly Detection Engine is a Compute-to-Data algorithm designed to identify extreme consumption spikes, periods of technical inactivity, and unusual energy export events. It provides a secure and actionable early diagnostic that supports preventive maintenance strategies, reduces operational risk, and improves system reliability without exposing sensitive data.

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