Time Series Aggregation in Power System Studies in the Presence of Wind Energy: A Matrix-Profile Perspective
Bibliographic record
Abstract
Accurate time aggregation of renewable energy and electricity demand data is essential for effective planning, forecasting, and decision-making of modern power systems. However, two critical assumptions in time aggregation have been overlooked in the existing literature, underpinning the accuracy and validity of aggregated data. The two assumptions are: (i) wind power has cyclic behaviour, and (ii) the general behaviour of wind power stay the same from one year to another. This paper aims to address these gaps by shedding light on the cyclical behavior of wind power data and evaluating its year-to-year consistency. To achieve this, two algorithms are designed to explore the cyclic patterns inherent in wind power data and their robustness from one year to the next. These algorithms are then applied to the comprehensive dataset provided by the Electric Reliability Council of Texas (ERCOT). By analyzing the results of our investigation, valuable insights are gained, opening up new possibilities and avenues for future research in this domain.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".