Electric Vehicle Energy Consumption Forecasting with Data-Driven Approaches
Bibliographic record
Abstract
Electric vehicles (EVs) are gaining significant traction among consumers due to technological advancements and improvements in battery capacity. However, the increasing penetration of electric vehicles presents a challenge for modern power grids. To effectively manage the future energy consumption of EVs, it is essential to develop robust forecasting models. This paper implements the Holt-Winters model, Monte Carlo Simulation, and Polynomial Curve Fitting model using daily energy consumption data from Newfoundland and Labrador Hydro, as well as the Government of Newfoundland. These models are applied to predict future EV adoption trends and associated energy consumption. By utilizing actual historical data, the parameters for each model are configured and optimized. The analysis is conducted using the MATLAB Curve Fitting Toolbox, Statistics and Machine Learning Toolbox. The results suggest that EV energy consumption in Newfoundland and Labrador is projected to increase from 151.33 MWh in Q1 to 186.77 MWh in Q4 of 2025. Among the three forecasting models, the Holt-Winters Smoothing Model demonstrates the highest accuracy in forecasting performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".