01Power availability & capacity
- Problem
- Expansion needs may exceed available capacity.
- Data
- Contract capacity, connection conditions, historical peaks.
- Decision support
- Compare capacity scenarios and expansion constraints.
KiWi Core Technology / AI-EDC
AI Data Center Energy Operating System
Connect supply-demand analysis, renewable matching, storage collaboration and traceable records through the AI-EDC energy data architecture—supporting energy planning and operational decisions for AI data centers.
Concept artwork · Not a KiWi-owned facility or actual customer site
Technical framework × Application validation
AI-EDC means AI Energy Data Center. The existing technical framework and retail evidence or simulations do not establish AI data center production capability. This page presents an application and field-validation direction.
01 / AI-EDC
From energy questions to informed decisions
Compute load is only the starting point. Supply, facilities, contracts and records need a shared energy perspective.
02Reliability & risk
- Problem
- Backup conditions and supply events are fragmented.
- Data
- Supply events, UPS status and maintenance records.
- Decision support
- Surface risk signals for assessment by site teams.
03Variable compute load
- Problem
- Changing workloads reshape electricity demand.
- Data
- GPU workload summaries, meters and job schedules.
- Decision support
- Forecast load ranges and suggest schedules.
04Cooling & facility visibility
- Problem
- IT and facility energy can be difficult to reconcile.
- Data
- Cooling energy, temperatures and facility meters.
- Decision support
- Identify relationships and data gaps without directly controlling equipment.
05Renewable time alignment
- Problem
- Renewable generation and demand may not coincide.
- Data
- Supply curves, load profiles and contract conditions.
- Decision support
- Compare time alignment and potential gaps.
06Cost & record traceability
- Problem
- Tariffs, settlement and decision evidence are scattered.
- Data
- Tariffs, contracts, settlement and data versions.
- Decision support
- Compare cost scenarios with traceable supporting records.
GPU, cooling and UPS are data and operational context—not claims that KiWi directly controls them.
02 / AI-EDC
Six layers. One energy perspective.
The existing AI-EDC technical abstraction extends toward AI data center validation. Explicit inputs, processes and outputs connect each layer.
AI-EDC / 01
Devices & edge collection
- Inputs
- Meters, sensors and equipment status.
- Process
- Sample, buffer and check quality on compatible edge devices.
- Outputs
- Readings with source and timestamps.
Data flow
Authorized data integration and analysis—not physical electricity movement.
Physical electricity flow
Determined by the grid, equipment and applicable contracts; illustrated links are not a delivery commitment.
Conditional control
Requires compatible devices, permissions, integration and field validation; decision support is the default.
Cloud / edge collaboration depends on compatibility, permissions, integration and field validation. The AI data center application must not be read as an established production deployment.
03 / AI-EDC
A 15-minute planning loop connecting data and decisions
- 01 →
Data
Align consumption, supply, status and contract data.
- 02 →
Predict & Match
Compare load forecasts, supply windows and constraints.
- 03 →
Dispatch & Exchange
Provide scheduling / exchange decision support for authorized teams or systems.
- 04 →
Proof & Dashboard
Retain sources, decision versions and execution feedback.
15 minutes is a planning / data granularity—not a guarantee of forecast accuracy, control response time, autonomous physical dispatch or reliability. “Dispatch & Exchange” means decision support unless a compatible, authorized system is field validated for control within the project scope.
04 / Concept interface / Demo data
Bring the energy context into one view
Synthetic scenarios—not live customer data, actual facilities or quotations, and not verified savings. Power is in MW; interval energy is in MWh. Times refer to local time on a demo day.
Load13MW
Renewables7MW
Grid supply6MW
Storage discharge0MW
Starting SOC70%
Ending SOC70%
Load & renewable curves (MW)
— Load— Renewables
Interval energy balance
13 MW = 7 + 6 + 0 MW / 12:00–12:15
Load = renewables + grid + storage discharge. Demo storage capacity: 10 MWh; discharge efficiency: 95%. SOC drop = discharge MW × 0.25 h ÷ 0.95 ÷ 10 MWh × 100. Each interval is an independent scenario, not a continuous dispatch commitment.
Curve data & interval state
| Time | Load (MW) | Renewables (MW) | Grid supply (MW) | Storage discharge (MW) |
|---|---|---|---|---|
| 08:00 | 12 | 2 | 10 | 0 |
| 10:00 | 12.5 | 5 | 7.5 | 0 |
| 12:00 | 13 | 7 | 6 | 0 |
| 14:00 | 13 | 6 | 7 | 0 |
| 16:00 | 12.5 | 3 | 8.5 | 1 |
| 18:00 | 12 | 1 | 10 | 1 |
Synthetic scenarios—not live customer data, actual facilities or quotations, and not verified savings. Power is in MW; interval energy is in MWh. Times refer to local time on a demo day.
05 / AI-EDC
Four value directions. No presumed performance promises.
Full visibility
Align IT, facility and supply data to identify gaps and dependencies.
Supply / storage coordination
Compare supply, storage constraints and load windows for conditional coordination planning.
Cost / risk assessment
Compare reviewable cost and risk scenarios with explicit assumptions and contract constraints.
Traceable records
Retain sources, versions and decision evidence for record preparation—not certification or reporting compliance guarantees.
KiWi New Energy
Start with your data and operating conditions
Discuss data availability, integration boundaries and validation scope with KiWi to define a reviewable next step.