Analysis of NetZero Energy Homes (NZEHs): Stakeholders, Design, and Performance
Bibliographic record
Abstract
NetZero Energy Homes (NZEHs) have emerged as a promising solution able to alleviate the energy strain that residential buildings exert on limited natural resources, thereby reducing the detrimental impact on the environment. Since the Government of Canada announced the NetZero energy healthy housing initiative in 2005, and the NetZero energy home coalition fostered the long-term vision that all new homes be built to net zero energy standards by 2030, many efforts have been made to realize this ambitious goal. Meaningful progress has been made in this regard; however, there still exist outstanding questions that must be answered: after the residential housing industry invests in the development of NZEHs, are customers willing to buy? What are the impacts of NZEHs on stakeholders? Based on the state of the art, how can NZEH design be improved? What are the effective means to improve the actual performance of NZEHs? In response to these important questions, this research is developed to achieve the following objectives: (1) to identify market acceptance and impacts on stakeholders of NZEHs through stakeholder analysis; (2) to investigate energy performance of design scenarios through energy simulation; (3) to assess and analyze the actual energy performance of NZEHs, based on sensor data collected using continuous monitoring; and (4) to conduct energy calibration and cost analysis in order to improve NZEH design by integrating the energy simulation, energy monitoring, and survey results. The holistic knowledge gained through the study and analysis can be employed to promote NZEHs, and to improve the design and operation of NZEHs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".