A computational analysis on energy consumption of a Ryerson building
Bibliographic record
Abstract
This project analyses the energy consumption of 44 Gerrard St. East. This site is primarily used as the Ryerson University Theatre School and it consists of four classrooms, seventeen offices, six studios, and two theatre auditoriums. Since it is a three-storey building, plus a basement, thus, the energy level for this building is supposed to be moderate. However, because it is an old structure, constructed back in the early 1940s, this building seemingly has considerable energy consumption. The main objective of this energy assessment is to reduce the building load. This goal can be achieved by simplifying and controlling certain parameters that directly and indirectly involve energy consumption. For example, indoor temperature and relative humidity can be maintained at low level in winter and at high level in summer. In addition, monitoring heat loss, heat gain, infiltrations through the building surrounds, and the level of illumination for various types of lights helps to reduce overall energy consumption. Several other factors such as operating costs, maintenance costs, and repair costs influence the energy management of the site. With the help of energy management software, eQUEST, the structure, outlook of all the walls, windows, roof and the type of HVAC system can be developed for analysis. Through eQUEST, various tasks such as heat transfer involvement, energy consumption load calculations and load balancing in comparison with energy saving guidelines will be discussed in detail.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".