Thermal Modeling and Electric Space Heating of a University Building in Newfoundland
Bibliographic record
Abstract
Buildings play a substantial role in global energy consumption, constituting a considerable share of the overall energy use. In Canada, they contribute to around 25% of the total final energy consumption. Notably, space heating emerges as the primary energy consumer, accounting for approximately 57% of energy utilization in institutional and commercial buildings. This paper presents a feasibility analysis of converting the space heating system of the Core Science Facility (CSF) building of Memorial University of Newfoundland (MUN). Analysis is done using RETScreen Clean Energy Management Software, known as RETScreen Expert, a software package developed by the Government of Canada, and the thermal modeling of the building using Energy3D, developed by the National Renewable Energy Laboratory (NREL). The feasibility study indicates that significant savings can be achieved if space heating is switched to electric resistive heating. The results indicate a 24.2% savings in annual energy costs, with a simple payback period of 10.5 years. The simulation results from Energy3D are compared with the measured building energy consumption data provided by the MUN Facilities Management Department. The thermal model indicates less energy consumption than the actual measured values, which is a result of transmission losses, the interconnection between the CSF building and the University Center, building occupancy, the ventilation system, and degradation of equipment that are not considered in the model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".