Streamlining long-term energy system scenario data annotation

November 5, 2025
Showcases

Streamlining long-term energy system scenario data annotation

by using the NFDI4Energy annotator with the Open Energy Ontology (OEO)

Np Close up of wind turbines on a table with data sheets, tablet and laptop computer plan blueprints 5r9Ok2 free

The Showcase’s Starting Point in Energy System Research

Long-term energy scenarios based on Energy System Optimization Models (ESOMs) provide detailed insights into possible pathways for Germany’s future energy system. A comparison across different models and studies supports better policy design and research transparency. However, such comparisons are challenging. Different studies use different variable naming conventions and model structures. This makes it difficult to align variables that represent the same concept—a process that often requires significant manual effort and hinders data interoperability.

Motivation and Research Requirements

Ontologically annotated datasets facilitate simpler and more automated scenario comparisons. Using a shared vocabulary like the Open Energy Ontology (OEO) allows for a consistent, machine-readable mapping of variables across different studies. However, this annotation process is time-consuming and requires specialized knowledge of ontologies. Our goal was to address this challenge by using NFDI4Energy services to make semantic annotation more efficient, reproducible, and interoperable.

NFDI4Energy Solution

We demonstrated a workflow for annotating large scenario datasets (using the IAMC format) by applying a combination of NFDI4Energy services.

The main challenge was to map scenario variables to the OEO, making them machine-readable and searchable via SPARQL queries for automated comparison.

Our process used the NFDI4Energy Annotator tool to automatically generate annotation suggestions. While these require human review, they significantly reduce the manual workload. We then used the TIB Terminology Service for manual review and proposed enhancements to the OEO when gaps were found.

This showcase prepares a large dataset for scenario comparison, helps facilitate the integration of other IAMC-format data, and provides documentation to support other researchers in their own annotation workflows.

Link collection

NFDI4Energy input

Task areas involved


Your contact person

Finn Peters

Albert Ludwigs University of Freiburg

INATECH

Researcher


[Image: Close up of wind turbines on a table with data sheets, tablet and laptop computer plan blueprints by Giuseppe Lombardo from Noun Project (CC BY-NC-ND 2.0)]