Forecasting Transmission Line Loss Using a Cluster-Based Refinement Framework and Scheduled Outage Data
Bibliographic record
Abstract
Transmission line loss forecasting is an important power system forecasting task, yet, existing methods often overlook qualitative operational data such as scheduled outages, which directly impact network topology and losses. This paper introduces a newframework that integrates scheduled outage reports with a two-stage cluster-based refinement solution to enhance forecasting accuracy. First, we process and utilize outage data in a way that preserves its temporal and contextual relevance, using Natural Language Processing (NLP) technique. This addresses a key gap in prior works that relies solely on quantitative inputs. Next, the proposed framework employs an initial baseline model in the first stage, followed by cluster-refinement using submodels trained on grouped data patterns. The proposed framework is applied to 24 hour ahead forecasts of transmission losses on an IEEE-118 bus test system, and in Alberta, Canada.We compare the results to benchmark methods from existing state-of-the-art transmission loss forecasting models. Our findings indicate that the proposed framework offers an accurate forecasting solution, outperforming the benchmark techniques. Moreover, these results highlight the value of integrating qualitative information into forecasting models for more accurate and reliable predictions.
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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 | 0.001 | 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".