Novel Quantification of Hourly GHG Emissions in Cold-Climate Buildings for Mixed-Grid Environments
Bibliographic record
Abstract
This thesis introduces novel methodologies and tools for calculating greenhouse gas (GHG) emissions, emphasizing Scope 2 emissions in cold-climate mixed-grid setting.It aims to refine the precision of GHG emission estimations, paving the way for more informed and effective emissions mitigation strategies.By evaluating existing practices and pioneering methods for calculating and forecasting GHG emission factors (EFs), this work addresses the unique spatial and temporal challenges associated with such environments.A key advancement presented is a novel methodology for calculating Consumption-based Hourly Emission Factors (CHEFs) that considers the electrical grid's spatial and temporal sensitivity.Applied in Ontario, Canada, this approach not only demonstrated potential savings of $13.3M in over-taxation for Ontario but also a significant 44% reduction in GHG emissions for a case study building compared to conventional methods. Further analysis comparing average EFs (AEFs) and CHEFs across archetype buildingsshowcased the superior accuracy of CHEFs, especially during peak grid demand.This led to the recommendation of a zonal approach to building codes, aligning electrification strategies with GHG savings across various scenarios in Ontario.Moreover, the thesis evaluates the discrepancy between AEFs and CHEFs in building operations, noting a significant 61% misjudgment in GHG savings estimation.This highlights the urgent need for temporally aligned and adaptable EF models. Innovative tools such as Emission Duration Curves (EDCs) and Emission Event DurationCurves (EEDCs) were introduced, revealing a moderate correlation between hourly energy use iii and GHG emissions.This suggests that peak energy loads and emissions peaks do not always align, advocating for custom sustainable building management strategies.Additionally, the efficacy of ARIMA models in forecasting CHEFs was examined, showing that these models, particularly for 1-hour and 24-hour forecasts, offer high predictive accuracy.This could greatly enhance operational planning and emissions management.This thesis contributes to the understanding and improvement of GHG emissions calculations in the building sector.It demonstrates the critical need for methodologies that are sensitive to both the spatial and temporal dimensions of electrical grid use, advocating for a shift towards more dynamic and precise approaches in environmental management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".