Energy performance of commercial buildings in partial-to-no- occupancy: Lessons learned from the COVID-19 pandemic lockdown in Canadian government buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it