Synchronization of water-energy consumption in residential and non-residential buildings during COVID-19
Bibliographic record
Abstract
COVID-19 has made working from home (WFH) a widely prevalent mode of work, resulting in highly complex changes of energy and water consumption in buildings. To understand these changes, this study applies the concept of water-energy nexus (WEN) in the analysis of energy and water data in residential and non-residential buildings in Ontario, Canada, before and during the pandemic. The study found the overall energy and water consumption of buildings exhibited a decreasing trend, with the most significant change found in water consumption. Energy and water consumption increased in residential buildings but decreased in non-residential buildings; the changes in energy and water consumption were synchronized over the WFH period. This study also elucidated that changes were related to the demographic and job attributes. When dealing with the peak load of residential consumption with a high consumption benchmark, due consideration should be given to the stronger synchronization of the two types of resources to improve the resilience of residences to cope with the uncertainty of unexpected large-scale public health crisis. Applying WEN to building resource consumption during WFH for the first time, the findings shed light on the need to enhance integrated water and energy management.
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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 | 0.000 | 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".