Adaptation to extreme weather events using pre-conditioning: a model-based testing of novel resilience algorithms on a residential case study
Bibliographic record
Abstract
Climate change increases the frequency and intensity of extreme weather events that can be a prominent cause of power outages in North America. These events may cause buildings to experience outages for hours to days, endangering occupant well-being. Although typical adaptive strategies can offer assistance, they often demand substantial initial investments. Thus, due to the need for low-cost solutions, this paper evaluates the efficacy of the proposed pre-heating/cooling algorithm using smart thermostats. The ongoing research employs automated energy modelling through Python scripting to streamline the energy model upgrade process and the EnergyPlus Energy Management System (EMS) algorithm to incorporate pre-conditioning features during grid outages. The results indicated an average 18% improvement in peak intensity and 9% in overall performance during extreme events. Also, it offers the potential for future studies to employ this methodology in assessing the effects of other low-cost strategies for adapting to grid disruptions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".