sweet-CROSS Logo

CROSS Data model

Shared semantics for comparable scenario data

Standard
Shared vocabulary
Defines content/structure, not the file type—YAML is common, JSON also possible
Taxonomy
Standard dimensions
Uses controlled dimensions like time, geography, technologies, sectors, and fuels
Versioned
Traceable evolution
Community contributions with stewardship and version control for transparency

The CROSS data model structures and categorizes energy-related datasets so that assumptions and results stay consistent, comparable, and interpretable across models and users.

About

CROSS Data model

Why it matters?

Energy-system models often use different variable names, units, and index conventions (time, geography, technologies).

The CROSS data model provides a community-agreed interpretation layer so datasets remain consistent, comparable, and interpretable across modelling teams and users. It lets teams map local variables to a common meaning in a documented way so results can be aggregated, compared, and understood with confidence.

What it enables?

Clear data exchange
Datasets remain understandable beyond the original modelling team

Reliable comparisons

Scenarios from different models can be compared consistently instead of manually reconciled

Automated validation

Shared validation rules make it easier to detect inconsistent submissions

Traceable evolution

 Version control and stewardship make changes transparent over time

Two core components

The CROSS data model is built from Semantics (meaning: "what is this variable?") and Taxonomy (standardised indices: "where/when/when does it refer to?").

Semantics

Taxonomy examples

Precise variable meaning

Variable description and context to assure different model represent the same quantity

Time dimension

Annual, monthly, hourly

Units

Unit of measurement for consistency

Geography

Spatial coverage (country, cantons, etc.)

Validation rules

Plausible ranges based on physical limits or expert judgment

Technologies

Harmonised names and hierarchies

Community process and versioning

The data model evolves through community contributions from modelling teams, with CROSS coordinating and curating updates to keep definitions coherent. Versioning supports traceability and transparent evolution over time.

Contributions

Teams propose new variables, dimensions, or refinements as needs emerge

Stewardship

CROSS maintains harmonisation and quality control of the shared vocabulary

Traceability

Version history makes changes auditable and comparable over time


Documentation

Current version

The CROSS data model defines standardised variables for energy system modelling, organised into model assumptions (inputs) and results (outputs).

Model assumptions

  • Macro-economic: Population, GDP, energy reference area
  • Demands: Space heating, hot water, process heat, transport
  • Resources: Fuel import prices, biomass potentials

Model results

  • Energy carrier production and use: Electricity, hydrogen, methane and liquid fuels
  • Energy supply: Heat, transport supply
  • Economics: System costs, carbon prices
  • Emissions: Carbon emission, carbon capture and storage
Go to documentation