Climate Change Impacts on Thermal Performance of Residential Buildings
Bibliographic record
Abstract
Climate change has altered regular temperature patterns and various climate variables on a global scale, causing growing concerns about future food, water and energy security. Immediate action should be taken to understand the extent of climate change while also proposing adaptation strategies to cope with the projected future climate conditions. From the energy security perspective, particularly in consideration of the ever-increasing human population, an important aspect to understand is the impact of climate change on the energy consumption of residential buildings. Understanding the impact of climate change on building energy consumption is not only beneficial for advising efficient energy-saving measures, but also for understanding future energy requirements. Various studies have already shown that climate change effects heating and cooling loads of buildings in various climates around the world. However, there is a lack of comprehension of the effects of climate change on energy consumption in Quebec. In addition, some of the methodologies employed to address the impact of climate change in buildings may be not be accurate or accessible to practitioners. The present study tries to fill this gap by advising a simple procedure that can be implemented in day-to-day engineering practice for a detailed understanding of the effects of climate change on building energy consumption. The methodology is applied to a residential building in Montreal, Quebec (Canada), using the state-of-the-art climate model projections for the periods of 2011-2040 (short-term future), 2041-2070 (midterm future), and 2071-2100 (long-term future) and under low and high greenhouse gas concentration scenarios. In brief, the available projections of five global climate models was studied for two particular weather parameters, namely dry-bulb temperature and shortwave radiation. The projections were downscaled at the point location and at an hourly resolution using a cascade model based on a quantile mapping bias correction method and a modified quantile-based k-nearest neighbor method. The downscaled projections were used as inputs to TRNSYS, an energy simulation software, in order to quantify the heating and cooling loads as well as judge the overall performance of the residential building in Montreal. This methodology can provide a basis for detailed understanding of the impacts of climate change on building energy consumption. Considering the applied case study, it is understood that climate change will not only change the intensity of the heating and cooling loads but can also change the empirical distribution of hourly energy consumption, particularly during peak loads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".