Residential Densification for Positive Energy Districts
Bibliographic record
Abstract
The energy footprint of houses can be reduced by replacing the aging stock with higher density and more energy efficient homes equipped with on-site renewable energy production. In this study, a “double density” simulation scenario is considered where each existing detached house in a community is replaced with two houses of equal living area on the same land lot. The new houses were assumed to be equipped with several energy efficiency measures (envelope, HVAC, and domestic hot water) and a building-integrated photovoltaic (BIPV) roof. The TRNSYS software was used to simulate the annual energy performance of the buildings in Montreal, Québec, Canada (45.5°N). It was found that the two new houses, which can accommodate twice the number of people on the same land lot, consumed 30% less energy than the existing house. Individually, each of the new houses required 65% less electricity than the existing house (reduced from 22,560 to 7,850 kWh yr−1). In addition, the BIPV roof installed on the two new houses could generate nearly three times more electricity (44,000 kWh yr−1) than they consumed (15,700 kWh yr−1). Annually, nearly half (44%) of the house's electricity can be directly supplied by the BIPV system. A significant portion of the annual solar electricity generation (84%), which cannot be directly utilized by the houses, can be stored on-site for later use to increase self-consumption (e.g., power-to-thermal energy or charging electric vehicles) or could be exported to the grid to support decarbonization elsewhere (e.g., production of hydrogen fuel for transportation). The combined effect of energy efficient construction and on-site renewable energy production would enable occupants to shift from consuming 5,640 kWh yr−1 to producing 3,540 kWh yr−1. Residential densification can significantly contribute toward retrofitting existing communities into resilient positive energy districts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".