A Data-Driven Load Fluctuation Model for Multi-Region Power Systems
Bibliographic record
Abstract
In this paper, we propose a data-driven load fluctuation model, based on high-resolution historical demand data from multi-regional systems, that can be used for research such as power system generation control studies and probabilistic load flow studies. As in previous studies, the random load fluctuations are modeled as independent Gaussian random variables; however, unlike in previous studies, we do not assume the relationship between the standard deviation and the base demand in each region is known a priori. Instead, we propose a framework for determining the relationship between the base demand level and short-term demand uncertainty. The developed framework has been tested using actual 5-minute demand data from the New York and New Zealand power systems. The results demonstrate that the proposed models outperform those used in previous work. Coefficients of the example cases are included, the parameters of which can be applied to similar multi-region systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".