Changes in energy use profiles derived from electricity smart meter readings of residential buildings in Milan before, during and after the COVID-19 main lockdown
Bibliographic record
Abstract
The COVID-19 pandemic had a profound impact on society, causing changes in various aspects of people's lives, including their energy use habits. This has prompted a need for checking and updating standard energy use profiles, particularly for residential electricity use. To address this topic, a study was conducted on 24 multifamily buildings in Milan, using clustering to extract patterns from a database of quarter-hourly electricity use data from 2019 to 2020. This study found an increase in electricity usage during the COVID-19 lockdown period for residential buildings, likely associated with the imposed restrictions. The research also highlighted a shift in energy usage from the morning peak to the central hours of the day during the working days of the lockdown period, while a gradual increase in electricity usage throughout the day and no morning peak was observed during the Autumn (post-COVID) period. The findings can assist regulators and businesses in weighing the benefits and drawbacks of remote working and provide modellers with a complete set of daily load profiles for an Italian residential case study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".