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See It Work
SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available CORE-ENERGY-MGT-AGT-001 AI Agent

Agentic Energy Management Agent (Efficiency Expert)

Continuously monitors energy consumption patterns, detects equipment issues through energy signatures before traditional symptoms appear, and optimises load scheduling against production demands, utility rates, and sustainability targets.

ManufacturingMiningOil & GasEnergy & UtilitiesWater & Wastewater Energy Management

Target outcome · Measurable reduction in energy costs and peak demand charges, with early equipment health detection through power quality analysis and improved ESG reporting accuracy.

Business problem

Manufacturing operations face mounting pressure to reduce energy costs while meeting sustainability targets and maintaining production efficiency. Traditional energy monitoring systems and manual analysis cannot adapt to dynamic production environments and often miss early indicators of equipment problems revealed through energy patterns — resulting in escalating energy costs, missed sustainability targets, and undetected equipment deterioration.

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Energy data is scattered across multiple meters and systems without integrated analysis. Power quality issues and energy anomalies are not correlated with equipment performance or production schedules. Organisations struggle to meet carbon reduction targets without compromising production, while ESG reporting requires comprehensive data that is often incomplete or inaccurate without automated aggregation.

What it does

The Energy Management Agent is an autonomous Decision Agent that uses Composite AI — combining power quality analysis, anomaly detection, optimisation algorithms, and carbon footprint calculation — to continuously monitor energy consumption, identify equipment degradation through energy signatures, and optimise load scheduling.

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It integrates with Energy Management Systems, Building Automation Systems, and utility meters to provide explainable recommendations that align energy efficiency with production schedules, utility rate structures, and corporate sustainability targets.

Current process vs. with AI Agent

TODAY · ENERGY MANAGEMENTREACTIVE
×
Peak demand managementManual load shedding triggered reactively after demand threshold is exceeded
×
Equipment health via energy signalsEquipment issues identified through vibration or temperature alarms, often after degradation is advanced
×
Sustainability reportingMonthly manual compilation of energy data for ESG reporting; significant lag and error risk
×
Energy optimisation decisionsFacility managers apply generalised rules without real-time visibility into equipment and production interaction

Outcomes and measurement

Energy cost reduction

Baseline No systematic optimisation; peak demand charges incurred reactively
With agent Measurable reduction in energy costs through proactive load management and consumption optimisation

Equipment issue lead time via energy signals

Baseline Detected at traditional symptom stage
With agent Detected through energy signature analysis before conventional alarms trigger

ESG reporting accuracy

Baseline Manual, lagging, and error-prone monthly compilation
With agent Continuous automated Scope 2 tracking with integration capability for upstream ESG platforms

Peak demand penalty avoidance

Baseline Penalties incurred due to reactive demand response
With agent Significant reduction through predictive load scheduling aligned to utility rate structures

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Other

Ingests real-time and historical energy data via XMPro Data Stream Designerutility billing data and rate structuresequipment performance indicatorsenvironmental conditionsrenewable energy generation dataand sustainability targets

including energy consumption metrics

kWhpeak demandpower factor

power quality measurements

voltageharmonicsfrequency

production schedules

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. What energy monitoring systems, smart meters, and building automation platforms are available for integration with the agent?
  2. What are your current peak demand charge structures, and do you have time-of-use utility rate schedules that the agent can optimise against?
  3. Which equipment types are highest priority for energy anomaly detection, and do you have baseline energy profiles for them?
  4. What are your carbon reduction or ESG targets, and which reporting standards or upstream platforms do you need to connect to?
  5. What autonomy level is appropriate for load management actions — advisory recommendations only, or bounded autonomous adjustments within defined parameters?

Want our AI to walk you through these scoping questions?

SPEAK WITH OUR TEAM

Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Agentic Energy Management Agent (Efficiency Expert) fits your operations, what data you'd need, and what a scoping engagement typically looks like.

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