Energy analysis using carrier HAP program and Deep Lake Water Cooling (DLWC) feasibility analysis for Ryerson University
Bibliographic record
Abstract
The objective of this project is to determine the total annual energy summary in terms of cost and Greenhouse Gas (GHG) emission of 16 buildings at Ryerson University (RU). In addition, the Deep Lake Water Cooling (DLWC) feasibility analysis of RU is another objective of this project in terms of total energy consumption and amount of gas emission reduction. The total audit area of RU was 86% of the total campus area. Building energy simulation program, Carrier HAP (Hourly Analysis Program), has been used to make an integrated evaluation of building energy consumption. An energy simulation involves hour-by-hour calculations for all 8,760 hours in a year. In this project, an energy audit was conducted for the 16 existing buildings to establish the base case model, "Ryerson University", to determine its annual energy consumption across all usage. There are two sources of energy used at RU. Electricity uses for lighting, plug load, miscellaneous and cooling, and remote steam is used for cooling and heating. For the base case model, total energy consumption was 251 TJ. To reduce the total energy consumption of the base case model, HVAC systems were investigated to analyze their energy-based performance and impact on the GHG emission. There is no Heat Recovery Ventilation (HRV) system coming from the investigation of HVAC system. The sensitivity analysis was conducted using HRV system with air system. By using HRV system with air system, total of 5.6% energy would be saved for cooling and 76% energy would be saved for heating of RU. The energy intensity was determined to be 1.04 GJ/m² only for 16 buildings of RU and comparatively it is lower than other universities in Canada which have a range of 1.64 GJ/m² to 2.26 GJ/m². In the DLWC system, cool lake water at 4°C was used for building air conditioning. To reduce the cooling energy costs, DLWC system was considered as an alternative chilled water source. The Rogers Business Building (RBB) already has DLWC system. For DLWC system, chilled water was served by Enwave to the RBB. According to base case analysis of the RBB with conventional chillers, the electricity consumption was 924594 kWh for RBB due to chillers. With the implementation of DLWC system for the rest of the 15 buildings, total energy saving due to cooling would be 89.2% and GHG emission reduction would be 89% for CO₂, 70% for NOx and 70.4% for SOx due to elimination of chillers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".