01
Data Confidence
Validate completeness, freshness, range, drift, units and lineage before formal analysis.
G4UTechnical Services
Explore the assessment
G4UTechnical Services
AI Energy Management System
Professional algorithms calculate. Agentic AI connects the evidence.
The G4U AI Energy Management System combines engineering rules, physical models, statistics, machine learning and constrained optimization with evidence-grounded Agentic AI.
Managed performanceGovernance · M&V · Triple SLA
G4U AI Energy CoreEvidence · decisions · dialogue
Existing building systemsData and authorized site control
The decision core
More than another BMS. More than another chatbot.
The system operates above existing building systems and below the managed performance service. It creates governed diagnostics, forecasts, actions and performance evidence without replacing site protection or local control.
02
2D System Views
Explore system relationships, energy flow, operating state and evidence through configurable vector views.
03
Baseline & M&V
Compare candidate models, freeze approved references and preserve adjustments in a reproducible ledger.
04
Diagnostics
Detect performance drift, identify likely causes and quantify operational impact.
05
Forecasting
Anticipate load, expected operation and emerging performance or SLA risk.
06
Optimization
Evaluate strategies against energy, cost, indoor environment, reliability and equipment constraints.
07
Performance Ledger
Connect measures, actions, approvals, results and commercial value in one versioned record.
08
AI Energy Assistant
Retrieve evidence, compare periods, explain findings and draft structured actions and reports.
Hybrid intelligence
Different intelligence for different decisions.
Professional algorithms produce formal results. Local models support frequent, resilient analysis. Tool-using Agentic AI retrieves, compares and explains evidence across the system under explicit human authority.
Frequent local analytics
Rule, physics, statistical and machine-learning models operate close to the data where site readiness supports it.
Scheduled AI review
Daily or weekly tool-using investigations connect evidence across systems and time horizons.
Human authority
People approve formal baselines, M&V, SLA positions and higher-risk actions.
Evidence-grounded dialogue
Ask in business language. Inspect the engineering evidence.
The assistant plans a visible investigation, calls professional tools and brings the findings, supporting evidence and recommended actions together.
- Object contextCH-02 · Plant A · 14:00–18:0001
- Data quality97.4% evidence coverage02
- Performance model0.82 kW/RT vs 0.68–0.75 reference03
- Root-cause toolsPhysics · rules · anomaly · impact04
Evidence supports ranked causes
Chiller 2 efficiency deteriorated after 14:00 as condenser-water temperature rose while the lead/lag sequence held the unit at 62% PLR.
Review condenser-water reset logic
Validate tower approach against wet-bulb
Run an approval-gated shadow test
Boundary
The assistant can retrieve, compare, explain and draft. It does not silently change approved values, approve high-risk actions or act as the final M&V calculator.
Model Packs
Reusable engineering logic, configured for each building.
Each pack standardizes semantics, physics, features, rules, candidate algorithms, safety constraints and tests. Site topology, available points, operating modes and parameters are then calibrated locally.
MP-01Whole Building & Main Meter
MP-02Chillers
MP-03CHW / CW Pumps
MP-04Cooling Towers
MP-05Air-Handling Units
MP-06Fresh & Exhaust Air
MP-07Owner-Controlled Common Areas
Safe authority
Automation earns authority through evidence.
Progression is explicit, approval-gated and reversible. The same safety hierarchy applies at every level.
Boundary
The AI energy system does not write directly to the BMS. Authorized site controls remain responsible for command execution, interlocks and feedback.
Technical selection