Day-Ahead Dynamic Thermal Line Rating Using Numerical Weather Prediction
Bibliographic record
Abstract
One of the factors limiting the capacity of a transmission line is the thermal limit, which is given by the physical parameters of the conductor and by the ambient conditions. Typically, the capacity is calculated for the worst-case thermal conditions and is then set as a static limit. While this approach is easy to implement, capacity is wasted because the worst-case conditions occur infrequently. Dynamic Thermal Line Rating (DTLR) is a technology developed to overcome this problem by changing the rating in real-time in response to the current environmental conditions. This approach is effective and allows for full utilization of the capacity of the conductor. However, extensive monitoring equipment, communication infrastructure and support in the management system of the operator is required. This paper presents an alternative approach, where the rating is set daily based on a Numerical Weather Prediction generated the previous day. This method is similar to a seasonal rating, also known as Quasi or Semi-Dynamic line rating, but differs in that it uses a very short time frame of one day, as opposed to a month or a quarter year. A statistical model is used to quantify the uncertainty in prediction to assure the reliability of the predicted ampacity. The method is tested on measured data from a transmission line located in southern Alberta, Canada.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".