Assessing The Impact Of The Climate Change In German Building Stocks
Bibliographic record
Abstract
This study investigates the impacts of future climate uncertainties in the new generation of future climate data sets according to AR5(5th assessment report of IPCC) on simulating the energy performance of buildings by studying the building stock in Germany (Potsdam). This work is based on two data bases, namely ‘Tabula web tool- European building’ and ‘EPISCOP’. Software IDA ICE was used to make comprehensive energy simulation of buildings. Four different climate models and two representative concentration pathways (RCP 4.5 and RCP 8.5) were used in the assessment. Simulations run for three 30year periods between 2010 to 2099. Effects of uncertainties induced by RCPs are thoroughly investigated for long time period. Through the comparison of energy simulation results, it is found that due to climate change, heating demand will decrease, however, cooling demand will increase. According to the results, for the second 30-year period, the heating demand decreases by 7% and cooling demand increases by 16%, compared to the first 30year period. By comparing the distribution of the data sets, it is also found that the uncertainty caused by the climate model has an estimated impact on the future heating (cooling) demand greater than the uncertainty caused by the time period. The change in heating demand due to climate change and uncertainty is relatively low and very large for cooling demand.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".