Ensemble Learning for Charging Load 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
Electric vehicles (EVs) can help reduce the dependency on fossil oil and increasing concerns on environmental pollution problems. However, due to the complex charging behaviors and the large charging demand, EV charging has imposed a large burden on the power system. The forecasting of electric vehicle charging loads can help address the above issues by providing power systems with the future load as a reference for energy dispatching. Machine learning methods have demonstrated their effectiveness for short-term load forecasting. Different from previous works, this paper proposes a novel ensemble learning-based forecasting model by combining three base learners including the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM) algorithms. Specifically, a linear regression (LR) algorithm is used to learn the weight of each base learner. The feasibility and advantage of our proposed model are demonstrated by experiments conducted on a real-world dataset and comparisons with the other four baselines.
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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