Data-driven power demand disaggregation to the substation level
Bibliographic record
Abstract
Detailed representations of power systems are critical for understanding the operational characteristics and flexibility offered by smart grid technologies. Many details of power grids, however, remain proprietary assets, limiting the transferability of research results based on reference test networks to the real world. This work develops and validates a new methodology to create representative demand profiles at the transmission substation level. The proposed methodology integrates system demand, power grid, and context layers—each utilizing publicly available data. In the demand layer, K-means clustering captures the core statistical time series characteristics of demand data, increasing computational tractability of the problem. The power grid layer establishes a substation contribution index to system level demand, considering the grid’s network structure. In the context layer, demographic data serve as a proxy of demand. Taken together, the layer information creates a substation demand index, which disaggregates the system level demand profile to each transmission level substation. The method is validated on data from the Alberta transmission system operator (AESO), with results showing a mean percent error around zero, and a maximum percent error at 10%. The hourly, disaggregated substation demand profiles are useful within larger modeling efforts, such as transmission expansion planning and hosting studies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".