Bibliographic record
Abstract
Globally, buildings account for 40% of total energy consumption and 33% of total greenhouse gas (GHG) emissions. In cold climates such as Canada, space heating systems are primarily responsible for energy consumption and GHG emissions in buildings. Using gas absorption heat pumps (GAHPs) instead of natural gas (NG) furnaces, which are commonly used for space heating in many parts of Canada, is expected to reduce heating energy consumption and GHG emissions. The main goal of this thesis is to determine whether GAHPs are feasible to be used for space heating and cooling in cold climates, including the effects of frosting. The feasibility of GAHPs is assessed by comparing the energy consumption, fuel cost and GHG emissions of a GAHP with that of a NG furnace and an electric air-source heat pump (electric ASHP). Comparisons are included for heating only and for combined heating and cooling. To compare the GAHP with other space heating equipment, a small office building is modelled and simulated using eQUEST and hourly weather data of several Canadian cities. A thermodynamic model of the GAHP is then used to estimate energy consumption, fuel cost and GHG emissions in each city, and these values are compared to the values obtained with other space heating and cooling equipment. This thesis shows that GAHPs may be a feasible option in some cold climate cities and that frosting has a negligible impact on GAHP performance. During heating operation, a GAHP has lower energy consumption and GHG emissions compared to a typical high efficiency NG furnace in all cities investigated. A GAHP has higher combined heating and cooling energy consumption than an electric ASHP. On the other hand, a GAHP may have lower cost and GHG emissions compared to an electric ASHP depending on the local climate, ratio of electricity to NG fuel costs, and GHG emission intensity of the electric grid. In two of the nine Canadian cities investigated (Vancouver and Montreal), the GAHP has higher fuel costs compared to an electric ASHP because the cost of electricity is low compared to the cost of NG in these cities. In two of the nine cities (Saskatoon and Edmonton), the GAHP has lower GHG emissions because the emission intensity of the electric grid is high in these cities. The thesis also develops a method to predict if a GAHP will have lower fuel cost and GHG emissions compared to an electric ASHP in other cities without detailed simulation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".