MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it