Track facility-level emissions in real time
In electricity generation, natural gas distribution and renewable energy facilities, emissions must be monitored hourly and at the facility level. CarbonSmart pulls data directly from your IoT devices, calculates with regional grid factors and automatically produces ISO 14064 compliant reports. It also offers a dedicated module for machine learning–driven Scope 2 reduction.
The Sector's Real Problems
From data collection to reporting — every step breaks
Facility-level emission data scattered across different PI/SCADA systems
Inability to reflect hourly grid mix in reports
Comparative impact analysis of renewable projects performed on static spreadsheets
Scope 2 reduction plans based on assumptions rather than data
Platform Features
Sector-specific, end-to-end integrated
Real-time data
Hourly/minute-level consumption and generation data over PI, OSIsoft, SCADA and OPC-UA. Dashboards update live.
IoT integration
Ready-made connectors for meters, inverters and turbine control units; low-code integration for new devices.
Location-based grid factor
TEİAŞ for Turkey and country/region electricity mix factors abroad are applied automatically; location-based and market-based Scope 2 are calculated in parallel.
ML-driven Scope 2 optimization
A machine learning module developed from CarbonSmart's TÜBİTAK-funded research; matches generation profiles with grid carbon intensity to produce a reduction plan.
Facility comparison
View kgCO₂e/MWh performance of thermal plants, solar farms, wind farms and hybrid facilities side by side; real emission data for investment decisions.
Success Story
TÜBİTAK Project — 10% Scope 2 Reduction in Automotive
Through a TÜBİTAK-funded R&D project, ML-based optimization of Scope 2 emissions was implemented in automotive manufacturing facilities. The same methodology was adapted for the production planning of energy sector clients.
An optimization model was built by combining line-level electricity consumption profiles, regional TEİAŞ carbon intensity and operational constraints. The model recommends shifting load to the "lowest-carbon hours" while preserving cost and productivity. In the automotive sector pilot, Scope 2 emissions were reduced by an average of 10%; the module is today integrated into the production and sales planning of our energy sector clients.
10% reduction in Scope 2 emissions (automotive pilot)
ML-based load shifting + grid mix optimization
Operational efficiency steady, carbon costs declining
Live in production at our energy sector clients
Standart Uyumu
Every report audit-ready
Veri Kaynakları
Global emission factor libraries
- TEİAŞ electricity generation/consumption
- EPİAŞ Transparency Platform
- IEA electricity mix
- EPA eGRID
- UNFCCC Ulusal Envanter
