Techno-Economic and Environmental Performance Comparison of Different Systems for Space Heating Systems in Cold Climates – Case of the Bow Valley Municipalities
Bibliographic record
Abstract
Abstract This study aims to investigate the feasibility of utilizing shallow geothermal systems for space heating and cooling in the Bow Valley region of Canada. Three building types, including a duplex, an apartment and a hotel, were considered. To evaluate the energy demand of each building type, building energy models were developed using BEopt™ and validated using actual gas consumption. To meet the energy needs of each building, the performance of various heating systems, such as forced-air furnaces, air-source heat pumps (ASHP), groundwater heat pumps (GWHP), ground-source heat pumps (GSHP), and electric resistance heaters, was evaluated. The analysis was based on realistic input data, geological information, and location-specific conditions. The results showed that GSHPs are the most energy-efficient heating system, followed by GWHPs, cold climate ASHP, conventional ASHP, electric resistance heating, and gas furnaces. In terms of CO2 emissions, GSHPs show the lowest emissions. For the duplex building, a GSHP emitted 44 tCO2 compared to 289 tCO2 for the high-efficiency gas furnaces. The economic viability of each system varied depending on factors such as location and natural gas prices. Compared to high-efficiency gas furnaces, the results showed that the payback period for GSHPs and GWHPs ranged from 15 to 40 years, while it was 15 to 30 years for ASHPs and cold climate ASHPs, depending on available grants and incentives and local energy prices.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".