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Agent · Lingxi · Yuanheng

GAEnergy / Power

Energy Agent

AI augmentation on top of 8 years of energy management

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LINGXI · YUANHENG

Energy Agent · Plant B3 monitor

ANOMALY

Alert trigger

Plant B3 peak load spiked +38% at 23:00 last night — triggering multi-variable attribution.

48h load curve

-48h↑ 23:00 peaknow

Attribution trace

Fetch SCADA · 48h window

0.15s

Multi-var attribution

0.22s

Compare p95 baseline

0.09s

Diagnosis

Cooler #2 missed scheduled stop + idle exhaust fan. Adjust DCS schedule — est. ¥42k/month saved.

91%

Diag. acc.

4.2万¥

Monthly save

0.5s

Alert latency

SCADA connected

DCS ticket hook

№ 01 · Scenarios

What it solves in real business

Park-level anomaly detection

An industrial park reports consumption every 15 min. The agent flags anomalies against historical curves + operating conditions, catching 20+ leakage events monthly — avoiding millions in annual loss.

One-sentence energy report

Plant manager asks — "who exceeded consumption in Workshop 3 this month?" — and gets results split by shift / process, skipping the IT reporting pipeline.

№ 02 · Workflow

How the agent runs end-to-end

Trigger

User intent / system event / schedule

Retrieve

Private KB + business APIs

Reason

Best-in-class model + industry Skills

Act

System writes / human approval hook

Response + audit + feedback loop

№ 03 · Integrations

Plugs into the systems you already run

Electricity / water / gas / heat meters

IoT platform / SCADA

MES / ERP

Power spot market

Time-of-use tariff rule base

№ 04 · Guardrails

How we handle failure, over-reach, hallucination

Edge inference

Anomaly detection runs on edge — data stays within the site, meeting energy-sector compliance.

Physics constraints

The agent won't issue schedules that violate physics. Dispatch strategies go through operator approval before hitting PLCs.

Alarm suppression

Duplicate alarms within 30 min are suppressed to avoid alarm fatigue. Severity tiered — highest tier paged to ops lead directly.

№ 05 · Detailed capabilities

Going deeper

Background

8 years of field work on energy management — meters, collection, platforms, rules. The energy agent layers AI on top: diagnostics and optimization suggestions.

Core capabilities

Anomaly detection: consumption outliers against historical curves
Savings recommendation: suggestions grounded in working conditions and tariffs
Natural-language reports: ask, don't pipeline
Equipment health: failure prediction from IoT telemetry

Typical customers

Industrial parks, manufacturing plants, commercial complexes, municipal utilities.

№ 06 · Next step

Want to see this agent run on your business?

30-min scoping → 1-week proposal → 6-week PoC. No hard sell.

Book a 30-min consultation