Net-Zero Retrofit of Commercial Buildings in Cold Climates: A Case Study from Canada
Bibliographic record
Abstract
The Canadian government has committed to achieving net-zero emissions by 2050. In response, many organizations committed to exploring and implementing necessary measures and technologies to achieve net-zero buildings. By 2050, majority of Canada's building stock will already exist. Therefore, in addition to designing and constructing new net-zero buildings, it's crucial to develop effective approaches for retrofitting existing buildings. This task can be particularly challenging in colder climates due to the high heating load, primarily met by natural gas, and reduced solar generation during the heating season. Hence, efforts need to focus on significantly reducing overall energy use, maximizing the use of renewables, and electrifying end uses through the integration of appropriate building technologies. In this paper, we outline a recent net-zero retrofit carried out in a commercial building located in Ontario, Canada. The building under study incorporates three types of solar panels, a closed-loop ground-source heat exchange system, a highly efficient lighting system, and dynamic smart windows. We analysed two years of hourly electricity end-use and utility billing data to verify the energy performance of the building and provide insights into the lighting, plug, and heating and cooling electrical loads during pre- and post-retrofit periods. Additionally, we presented the performance of the solar electrical system and its impact on achieving net-zero performance in colder climates. The post-retrofit heating and cooling loads emerged as the largest electrical end-use, accounting for over 70% of the building's total electricity use. We also compared the pre- and post-retrofit energy performance against benchmark values and studied the cost and environmental impacts of the retrofit. The results showed that the building's pre-retrofit energy use intensity was nearly twice as high as the benchmark values. It is essential to consider this when applying the findings to similar commercial buildings undergoing net-zero retrofits. Ultimately, we determined this building achieved savings of more than $148,000 and 120 tonnes of CO₂ₑ for utility cost and operational carbon emissions per year, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".