Effects of the Human-Mobility Change during the COVID-19 Pandemic on Electricity Demand
Bibliographic record
Abstract
This study investigated how the changes in the number of people’s visits to various locations during the COVID-19 pandemic influenced electricity demand. Using the autoregressive distributed lag (ARDL) model, the study reveals that while an increase in visits to groceries, pharmacies stores, and transit stations increased electricity demand, an increase in the hours people spent at home had a negative impact on the demand during the period of the state of emergency (SOE). This study also demonstrates the differences in the effects of human-mobility changes on electricity demand among different SOE periods, which was probably related to the weakening in the effect of the SOE measures as people became accustomed to the SOE announcements. As the energy transition unfolds across the world, ensuring the optimization of integrated energy systems has never been more important; thus, the current study suggests that controlling human mobility could be one of the options through which policymakers could intervene in the energy-demand sector.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".