A Sensitivity Analysis Method for the Update of the National Energy Building Code of Canada (NECB-2017)
Bibliographic record
Abstract
An increasing trend in energy consumption can be seen worldwide. Projections for the world energy consumption indicate an increase of nearly 50% by 2050. In Canada, the electric power selling price has risen by 250% in the last four decades. The rising trend in energy consumption and cost is a pressing concern. Within that trend, residential, commercial, and institutional buildings are big contributors, accounting for 28% of the total secondary energy use in Canada. As a direct response to the increase of energy use in buildings, minimum energy efficiency requirements were proposed and compiled into energy standards, seeking to provide guidelines and instructions in the design, construction, and operation stages. These standards proved to be a powerful tool to improve energy efficiency, especially if adopted by state and federal legislators as mandatory requirements. Given the major role that such standards play, attention is drawn to the process used in updating these energy efficiency requirements. This research proposes a method for identifying the most impactful factors in the energy efficiency of buildings and for quantifying the impact that changes in these factors have on a range of energy related KPIs. This method can help policy makers and parties involved in the update of building energy code requirements by providing a metric to prioritize changes based on their impact. Additionally, the proposed method can aid in the allocation of R&D resources in the proposal of improvements to the building envelope and HVAC equipment, based on the impact of each of the studied improvements.
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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 | 0.001 |
| 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".