Data model

CROSS Data model

Shared semantics for comparable scenario data

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

Shared vocabulary

Standard
Defines content/structure, not the file type—YAML is common, JSON also possible

Standard dimensions

Taxonomy
Uses controlled dimensions like time, geography, technologies, sectors, and fuels

Traceable evolution

Versioned
Community contributions with stewardship and version control for transparency
01 Reason

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.

02 Purpose

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 and comparable over time
03 Structure

Components

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

Semantics

The meaning of a variable, expressed through standardised concepts and relationships to ensure consistent interpretation across datasets and models.

  • Precise variable meaning
    Shared meaning and context to ensure variables represent the same quantity across models.
  • Units
    Unit of measurement for consistency
  • Validation rules
    Plausible ranges based on physical limits or expert judgment

Taxonomy

Standardised dimensions that describe how each variable is indexed and interpreted across datasets.

  • Time
    Temporal resolution and coverage, e.g. annual, monthly, daily, hourly, or representative periods.
  • Geography
    Spatial scope and resolution, from national to regional, municipal, or grid-based levels.
  • Technologies
    Common classification for energy technologies associated with a variable, such as solar PV, wind, batteries, or nuclear for electricity supply
  • End-use
    Standardised definition of end-uses for different energy carriers, e.g. space heating, mobility, or electrolysis for electricity use.
04 Community

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 emergy

Stewardship

CROSS maintains harmonisation and quality control of the shared vocabul
05 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
Data model