Integrating Climate Change and Power System Resilience in the Canadian Building Sector Using Monte Carlo Model of Heating and Cooling Demand
Bibliographic record
Abstract
This paper aims to examine the impact of rising temperature brought by locale climate change on patterns of electricity demand. In this study, temperature response function combined with Monte Carlo method was implemented to estimate logarithmic heating and cooling demand in the Canadian building sector. The changes in climate variables are examined by analyzing downscaled data from twenty-six General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) for the city of Victoria Canada to the year 2070. The findings of this study suggest heightened responsiveness to high temperatures where the effect is offset by lower heating demand: the expected change in average cooling and heating demand of 2.7% and -2.5%, respectively. In addition, through sensitivity analysis, predictors other than climate variables, such as air conditioner and electric heater penetration and residential shares are identified as significant to the percent change of electricity demand. We find broad agreement with literature that overall demand in colder countries, e.g. Canada, is expected to rise slightly. However, peak demand is expected to increase by 20.2% with the increased AC penetration scenario and occurs during afternoon in the intraday pattern. This study assists decision-makers in energy resilience planning on how to manage the impact of climate change on energy system, such as technology selection for fulfilling additional capacity requirement and its cost implication, and on when the best time to adapt.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".