In-situ evaluation of a fuel-switching heating system in Edmonton, Alberta, Canada: Lessons learned
Bibliographic record
Abstract
In the residential sector, energy consumption and greenhouse gas (GHG) emissions remain key issues. Heat pumps are an emerging technology that shows promise of addressing both these issues through the electrification of space heating. Cold climates pose a unique challenge to specifically air-source heat pumps (ASHPs) due to reduced efficiency at lower ambient temperatures. A solution to this is fuel-switching heating systems, which can potentially reduce utility costs, GHG emissions, or energy consumption through the optimized utilization of multiple fuel sources for space heating. This study aims to investigate the energy efficiency and GHG emissions of a fuel-switching heating system composed of an ASHP and tankless water heater (TWH) in the city of Edmonton, Alberta, which is characterized by a cold climate. The key lessons learned from this in-situ evaluation were (1) The studied units consumed 13,361 kWh (52.7%) less total energy and emitted 3364 kgCO 2 eq (62.8%) fewer GHG emissions compared to the average rowhouse in Alberta. Despite exhibiting promising reductions, caution is required before implementing increased electrification measures in regions with high-pollution generation methods, such as Alberta. (2) The efficiency of fuel-switching systems is dependent on the controller. It was found that half of the studied units never utilized their ASHP for space heating, and that these units consumed 14.5% less HVAC energy compared to units with regular ASHP heating. This was caused by poor controller configuration, where the ASHP and TWH were operated on a single control loop, leading to the ASHPs being utilized in extreme cold conditions, and (3) Occupant behavior has a significant impact on energy performance between units. When investigating an occupant change for a single unit, the new occupants were found to consume up to 2.07 times more plug energy and 1.50 times HVAC and DHW energy during periods with similar weather conditions.
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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 | 0.001 | 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".