Assessing Building Energy Demand and Opportunities under Climate Change: A Data-driven Decision Support Tool
Bibliographic record
Abstract
Buildings are increasingly vulnerable to climate change impacts, including shifts in heating and cooling demand and accelerated degradation of critical systems. This paper presents the Resiliency Opportunity Assessment and Response (ROAR) tool, a data-driven decision support platform designed to assess building energy demand and component vulnerabilities under future climate scenarios. Unlike conventional simulation software, ROAR integrates stochastic modeling of energy demand with degradation processes, risk matrices, and cost– benefit prioritization of adaptation actions, while requiring only minimal input data (utility records, building age, and component investment values). The tool was applied to two pilot buildings in Victoria, Canada: the Davidson Office Building and the Uptown Shopping Center. Results show that warming trends are likely to decrease heating demand but substantially increase cooling demand, where cooling becomes the dominant energy driver by mid-century. HVAC degradation under rising loads was identified as the primary risk, with targeted retrofits and envelope upgrades providing significant potential savings. Validation against measured billing data and EnergyPlus simulations demonstrated that ROAR is able to reproduce building-level energy responses with strong agreement, confirming its applicability as a lightweight yet robust alternative to physics-based models. These findings highlight the tool’s potential to support building managers and policymakers in planning cost-effective adaptation strategies across portfolios of assets under climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".