Energy performance of commercial buildings in partial-to-no- occupancy: Lessons learned from the COVID-19 pandemic lockdown in Canadian government buildings
Bibliographic record
Abstract
Following the global shutdown as a measure to contain the spread of the COVID-19 pandemic, the world has witnessed a temporary decline in energy usage, especially in the commercial building sector. However, the magnitude of decline in that sector was not as large as the expected decline for unoccupied spaces. Energy performance of low-/unoccupied commercial buildings coupled with the new minimum requirement for outdoor air intake is an intriguing research question. However, occupancy data is expensive to obtain and is challenging from a privacy standpoint. Instead, by comparing the business-as-usual electricity usage with that of the known unoccupied period during the early stage of the lockdown, a wide spectrum of hybrid work electricity usage can be estimated. In this study, two years of hourly energy (thermal load-free electricity) use data for 49 commercial buildings equipped with smart energy management systems are analyzed to quantify those changes. A linear regression predictive model to estimate low-occupancy electricity loads is conducted. Results indicate that the proposed model is promising and can be further improved for better repeatability.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".