Self-Attention Transformer Based Short-Term Load Prediction for Electrical Distribution Feeders
Bibliographic record
Abstract
With the acceleration of urbanization, climate change, and population growth, the global electricity demand shows a significant upward trend. Accurate short-term load forecasting (STLF) plays a vital role in optimizing the operation of the electrical distribution system. Although recent deep learning-based short-term load forecasting models have shown significant advantages, achieving accurate load forecasting remains a daunting challenge as power load demand is affected by many external environmental factors and the inherent defects of traditional forecasting models such as recurrent neural networks (RNNs) and support vector machine (SVM). In order to tackle this challenge, this paper proposes a transformer-based short-term load forecasting model. It takes loads in distributed feeders as forecasting objects and makes full use of the self-attention mechanism to capture the long-term dependency and complex nonlinear characteristics of load data. Experimental results show that the model performs well in processing complex time-series data and load fluctuations in different seasons. It has strong generalization ability and provides a new solution for forecasting distribution feeder load.
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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 | 0.000 |
| 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 | 0.001 |
| 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".