Capturing Changes in Residential Occupant Behavior Due to Work from Home in Japan as a Consequence of the COVID-19 Pandemic
Bibliographic record
Abstract
Since there is a possibility that work from home will become one of the major working styles in the era of a new normal after the COVID-19 pandemic, it is necessary to examine an effect of work from home on life of residents at home and on energy consumption. Therefore, a web-based questionnaire survey was conducted at the end of October 2020 for households in which any of the family members was a work from home worker. In regard to the changes in life schedule, work from home helped 21.5% of respondents to sleep 30 min more or even longer. This shows that the loss of commuting time to work due to work from home improved the health of respondents in terms of sleep. In terms of the changes in energy consumption, more than a quarter of households experienced an increase in electricity consumption. Around 40~50% of households whose monthly electricity consumption increased from last year were not aware of the reasons for the increase or realized it for the first time when they answered the questionnaire survey. This indicates that feedback using home energy management systems, etc., play an important role in promoting energy-saving behavioral changes. An analysis of the causes of the increase in energy consumption by households reveals that the increase in hours of use of air-conditioners is significantly correlated with the increase in household electricity consumption in August (p = less than 0.01). The large-scale questionnaire survey on Japanese housing during the COVID-19 pandemic in this study elucidated that work from home as a countermeasure against COVID-19 infection affected the residents’ lives and the energy consumption of each household. This paper also showed the causes of the increase in household energy consumption and the importance of feedback, such as HEMS, in promoting energy-saving behavior. These results are expected to help to improve health of residents and energy saving in the era of a new normal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".