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 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 | 0.001 | 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 | 0.001 |
| 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".