Modelling of a net-zero energy condo in a cold climate using an interdisciplinary design framework
Bibliographic record
Abstract
Abstract Developing buildings which generate the amount of energy that they use (energy net-zero) in cold climates has the potential to significantly reduce overall energy consumption and Greenhouse Gas (GHG) emissions in these locations. This study introduces a design methodology which first iteratively reduces building loads from a synthesis of architectural, engineering, and building science strategies to achieve a minimized annual energy consumption. Then this optimized load is generated via on-site systems, and finally the overall design is validated within the goals of sustainability and high performance (as compared to benchmark designs). This strategy was applied to a detailed design-based analysis of a mid-rise, net-zero energy residential condo in Toronto, Canada. The building was developed within this iterative and computational process that draws on literature from passive house design, R2000, and ASHRAE 90.1 standards - as well as existing efficient building designs across various climates. Modelling using the eQuest building energy simulation software, relevant weather data, and hourly use profiles yielded energy consumption data which informed design modifications. Loads were systematically reduced by optimizing across building orientation, building layout and footprint, building materials, and architectural modifications. Furthermore, the use of Heat Recovery Ventilation (HRV) and Variable Refrigerant Flow (VRF) HVAC systems reduced heating and cooling demand and emissions in this mid-rise multi-unit residential building (MURB). Energy demand was further reduced by designing a high-performance building envelope with an improved window-to-wall ratio, triple glazed windows, airtight building enclosure, and high R-value insulation. On-site energy generation via the rooftop and facade solar array allowed for net-zero operation. The proposed methodology yielded a building design with annual energy consumption and GHG emissions reduced by 54.3% and 95.3% respectively.
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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 | 0.000 | 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 | 0.001 |
| 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".