AI × Energy Management
Use AI to catch abnormal usage and forecast peak load, making energy savings and storage dispatch more precise.
Book a free consultation→Use AI to catch abnormal usage and forecast peak load, making energy savings and storage dispatch more precise.
Who it is for
- Factories that have meters or monitoring systems but are not making good use of the data
- Power users who want to forecast peaks ahead of time and avoid exceeding contracted capacity
- Sites with solar and storage that want smarter charge/discharge schedules
- Companies adopting ISO 50001 that need to keep tracking energy performance
What we do
Data collection
Inventory existing meter and equipment data, and add measurement points where needed.
Energy monitoring dashboard
Bring usage, demand, and generation data together on one screen.
Anomaly detection
Automatically find standby power draw, abnormal peaks, and efficiency decline.
Load and generation forecasting
Forecast next-day load and solar generation as the basis for storage scheduling.
Savings analysis reports
Regularly report savings opportunities and track results.
Management system integration
Data maps directly to ISO 50001 energy performance indicators.
How it works
Data inventory
Confirm available data and gaps.
Connect and collect
Set up data collection pipelines.
Analysis and modeling
Build monitoring and forecasting models.
Dashboard launch
Deliver monitoring screens and reports.
Continuous optimization
Adjust models and strategy based on results.
What you get
Energy monitoring dashboard
Real-time and historical usage, demand, and generation.
Anomaly and savings reports
Regular analysis results and improvement recommendations.
Forecasting models
Load and generation forecasts for scheduling.
FAQ
Can we do this without meter data?
You can start with electricity bills and Taipower demand data, then add meters as needed to increase data granularity step by step.
Is AI energy management related to carbon inventories?
Yes. Electricity is usually one of the largest emission sources, and accurate usage data makes Scope 2 calculations and tracking of reduction results more accurate.
Do we need to upload data to the cloud?
Not necessarily. Depending on data sensitivity, you can deploy on-premise and keep the data analyzed inside your company.
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Map your processes, find the work truly worth applying AI to, and set the order before building anything.
On-Prem AI Hardware
Based on your model, user count, and data volume, we pick servers, GPUs, and storage that are just enough.
On-Premise AI Deployment
Build AI applications on your own equipment, so your data never leaves the company.
From one kWh to one tonne.
Tell us about your situation. We reply with initial advice within 3 business days.