Do peak energy loads and peak GHG emissions correlate in buildings? Insights from emission duration curves and emission event duration curves analysis
Bibliographic record
Abstract
This research proposes and applies emission duration curves (EDCs) and emission event duration curves (EEDCs) in a novel way for comprehensive analysis of greenhouse gas (GHG) emissions in correlation with building energy consumption. Examining a 2018 high-rise building in Ottawa, we found a moderate Pearson correlation of 0.3 between hourly energy and GHG emissions, despite a significant p-value. This indicates that peak energy loads and emissions peaks do not necessarily align, underscoring the need for a new operational strategy. The EDC and Load Duration Curve (LDC) patterns further accentuated these differences, suggesting that electrical emissions are more concentrated in specific periods compared to electrical loads. For our case study, 12.04% of yearly GHG emissions occurred within just 1% of the year, contrasting with only 2.14% of the annual energy use. The study also highlighted the potential of a demand response event through a simulated voluntary outage during peak emission intervals as a viable GHG mitigation strategy. Collectively, these insights stress the importance of a comprehensive perspective on building operations, their interactions with the grid, and the necessity for tailored sustainable building management strategies moving forward.
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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 | 0.001 |
| 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".