MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations
Bibliographic record
Abstract
The penetration of electric vehicles (EV) has been increasing rapidly in recent years. Electric vehicle charging load poses a huge demand on the power grids. The forecasting for electric vehicle charging load, especially for the charging load of EV charging stations, is of significant importance for the safe operation of power grids. However, most of the existing forecasting methods fail to capture the long-term dependencies efficiently and assume the availability of a large amount of training data. Hence, they cannot address newly built charging stations with scarce historical charging load data. Meanwhile, most of the methods focus on point forecasting, which lacks risk consideration. In this work, we aim to leverage the benefits of Transformer-based models for EV charging forecasting. Specifically, we propos Probformer, a Transformer-based forecasting model for charging load forecasting. To enable Probformer to adapt fast to unseen environments, we further extend it to MetaProbformer, a meta-learning-based forecasting framework. Extensive experiments have been done on real-world datasets for both point forecasting and probabilistic forecasting. Experimental results show that our methods can consistently outperform baseline methods by a large margin.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".