Electric Vehicle Load Forecasting in Rural Areas: A Systematic Review
Bibliographic record
Abstract
The growing adoption of electric vehicles, combined with increasing interdependence between urban and rural areas, raises concerns about the resilience of electrical networks, particularly in rural regions where infrastructure is less robust and more limited in complexity. Accurate load forecasting is therefore essential to support effective planning and mitigate potential stress on the grid. This study aims to evaluate and synthesize methodologies for predicting electrical loads generated by electric vehicles in rural areas, with the objective of identifying current practices, data characteristics, and methodological gaps. Following a systematic review approach, the work compiles and analyzes recent literature to provide a structured reference framework for researchers and practitioners. The findings reveal a growing research interest in this field, particularly in Europe and North America, with both model-based and data-driven approaches used in comparable proportions, and short-term forecasting emerging as the most common horizon. However, a lack of standardization in the documentation of network characteristics remains a significant limitation across studies. The review contributes by clarifying the state of research, highlighting critical gaps, and offering guidance for future work. These results underscore the importance of developing standardized criteria for documenting network properties and integrating diverse data sources to enhance the accuracy and applicability of load forecasting in rural distribution networks.
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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 | 0.001 |
| 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 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".