Energy audit and base case simulation of Ryerson buildings using the "Carrier HAP" simulation program and prism analysis for energy consumption
Bibliographic record
Abstract
Modelling and simulation of energy consumption in 86% of the Ryerson campus was presented. Energy simulation models were developed with Carrier HAP for 16 Ryerson buildings. Carrier HAP, commercially available software, was used for the prediction of energy consumption and PRISM software was used for the energy consumption comparison in different locations using weather normal average temperature data. All of the possible sources and uses of energy in the building were accounted for in the modelling and simulation. From the simulation result, it showed that 26% of total energy was consumed by lighting and 19% of total energy used by plug load and 4% of total energy used by miscellaneous. Sensitivity analysis was conducted by reducing lighting schedule. As a result, annual energy savings of 10% for cooling load and 21% for hydro demand were achieved, but the heating load increased by 14%. The other part of the energy consumption was for the Heating, Ventilation and Air Conditioning (HVAC) system, 53% of total energy was demanded in this sector for the 16 Ryerson buildings. PRISM model was developed for compared Ryerson energy consumption and also compared Ryerson campus in different locations. The base case simulation result was compared with the campus planning actual consumption bill for the hydro, steam and DLWC cooling demand for the Ryerson campus. The result was under predicted from the actual bill. Simulation was under predicted hydro consumption by 5.7% and steam consumption by 6.26%. The average energy intensity was determined 1.04 GJ/m² for the 86% of total area of Ryerson campus. Also energy intensity (GJ/students) compared with different provinces in Canada, shows that Ryerson University consumed less energy and this value is 10 GJ/student.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".