Impact of climate change on the energy demand of buildings utilizing wooden prefabricated envelopes in cold weather
Bibliographic record
Abstract
• Prefabrication enhances the energetic performance of residential buildings. • Heating energy demand could decrease by 25 % in 2080. • Cooling energy demand could increase by up to 116 % in 2080. • Envelope junctions and airtightness significantly influence energy demand. • Significant contributions of prefabricated envelopes are seen at nighttime. Global energy demand continues to increase, and climate change is affecting the energy consumption of buildings. Wooden Prefabricated Wall Panel (WPWP) systems could represent a hygrothermally efficient solution to reduce buildings’ energy demand in the current and future climate scenarios. Therefore, this study aimed to evaluate and compare the impact of climate change on the energy demand of buildings utilizing prefabricated envelopes in the cold weather of Quebec, Canada. This study used dynamic simulations on a mid-rise residential building prototype for the 2020 climate scenario and the predictive scenarios of 2050 and 2080 (RCP 8.5 model), utilizing the software DesignBuilder and the EnergyPlus calculation tool. Simulations were conducted on the same building model using three different types of wooden wall systems separately: standard WPWP, optimized WPWP, and a traditional on-site wall system for reference comparison. Results indicate that WPWP systems consistently exhibit superior energy performance compared to the conventional envelope across all climate scenarios, with the optimized one showing the lowest energy demand levels. In all cases analyzed, heating demand decreased by approximately 25 % when comparing the 2020 period to 2080, while cooling demand increased by 91–116 %, depending on the building envelope. The total annual energy demand in each case showed reductions of 1–5 % projected by 2080. The most significant contributions to the envelope’s thermal performance by the WPWP systems were observed during the nighttime period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".