Automatic Generation of Real Power Transmission Grid Models From Crowdsourced Data
Bibliographic record
Abstract
Real models of electrical transmission grids are difficult to obtain. The process of generating such models from unstructured and incomplete data is tedious, and the resulting models are rarely updated. This paper proposes a novel approach for automatically extracting power-relevant data from the public and unstructured crowdsourced OpenStreetMap (OSM) and for generating topology and simulation-ready models of real transmission grids based on the relation between different grid elements such as power lines and substations. Our approach uses spatial analysis and minor assumptions to periodically generate transmission grid models based on the latest OSM data for every country on the planet. A comparison of our generated power grid models with official data from 14 countries reveals accuracy levels between 31% and 94%, caused by the varying availability of OSM data for different countries. Since the crowdsourced data is continuously improving, the automated and periodical model generation approach extends the models with new power circuits as the quantity and the quality of the OSM dataset increases. We provide a platform to access our generated models at open-gridmap.org. This paper describes our model generation method, presents our data access platform and evaluates the accuracy of our topological models for selected countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".