Energy

Asset Performance Agent

An asset that is slowly losing performance reports nothing. It only shows up in the monthly report.

Uses data to analyse the performance of energy assets.

Open agent
Public administration

Four processes that take up the most time today

Developed in Germany · Hosted in the EU

Developed in Germany · Hosted in the EU or on-premises

Delivered together with

STACKIT Sovereign hosting in Germany, single-tenant from the first user.
Rödl Legal, tax and compliance expertise for legally sound deployment.
4P Consulting Advisory and implementation for the public sector, starting with the AI readiness assessment.

Twelve assets, one monthly report, and the cause sits in a different file.

The readings flow into the control room, the maintenance jobs sit in the maintenance system, the yields come from billing. When a performance curve drops, someone has to line up the three data series over the same period and read through the fault reports alongside them. For twelve assets, that is a week's work every month.

12assetsTechnical operations is responsible for twelve generation assets.
18measured variables per assetOutput, availability, temperatures and meter readings add up to eighteen series per asset.
216time series per monthly reportTwelve assets times eighteen variables gives the number of curves someone would have to look at.
5 daysuntil the cause is foundThat is how long it typically takes to match a drop in a performance curve to a maintenance entry.

The deviation is identified on the day it occurs rather than at month end, which is where the 15% higher efficiency comes from.

The 15% efficiency gain is this agent's headline figure. The number of assets, the measured variables and the days to the cause trace the everyday work of a technical operations team and are illustrative, not measured.

Three steps through the process.

The agent puts the deviation, sensor history and maintenance entry side by side; any measure is carried out from the control room once the operations manager approves it.

Check sensor history against the target curve

The agent reads the asset's measurement series and compares them with the stored target curve. Every deviation gets a time stamp and a duration.

Asset WP-07, deviation from 14 June, lasting 9 days

Find maintenance records that match the deviation

The agent searches the maintenance system for interventions and fault reports from the same period. It names the entry whose timing matches the deviation.

Fault report 4471 of 13 June, pitch control readjusted

Pass a proposed measure to the operations manager

The agent compiles the deviation, the matching entry and possible interventions into a note and calculates the lost yield in kilowatt hours. The technical operations manager chooses the measure.

anlagennotiz_wp07_juni.pdf, lost yield of 41,300 kWh identified

What the agent does not do.

It does not intervene in the asset's control system.

It describes the deviation and the possible interventions. Any measure is carried out from the control room once the operations manager approves it.

It does not commission any maintenance.

It names fault report 4471 as the entry that matches in time. The technical operations manager decides whether a technician goes out to the asset.

It cannot tell when a sensor is giving false readings.

To the agent, a frozen data series looks like an unusual curve, not a device fault. Whether the value or the sensor is the problem is for the plant engineer on site to settle.

Connected systems

  • Control system
  • maintenance system
  • ERP system

Operations

  • Deployment modelEU cloud or private cloud
  • Data processingGDPR-compliant, no model training on your data
  • Responsible teamsPlant operations, engineering

The questions that come up before the first meeting.

How quickly is the Asset Performance Agent ready to use?

A pilot typically runs on your own data within 6 to 8 weeks. The agent connects to your existing systems rather than running as a separate, parallel environment.

Where is the data processed?

EU cloud or private cloud. Processing is GDPR-compliant and models are never trained on your data. Details in the Trust Center.

Where does the figure “15% higher efficiency” come from?

It is based on experience from comparable projects and process benchmarks, not a commitment. We work out the realistic effect for your organisation during the assessment before the pilot.

Related agents.

Intake review, case 2026/04711

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