Robustness study of a flexible zero-energy house
Bibliographic record
Abstract
The U.S. Department of Energy launched the 5th Solar-Decathlon-competition, defying student teams from all over the world to conceive a house powered exclusively by the sun. Team Belgium, of Ghent University, conceived the E-Cube, a modular and flexible house, that could be adapted depending on the inhabitants, the building site and the climate. This paper focuses on that last aspect: the robustness and flexibility of the energy concept and the design, depending on the climate it is built in. Different climates are selected for the analyses, reaching from climates with extreme winters (Canada: Saskatoon) to arid climates (US: Las Vegas), through milder climates (Belgium: Ukkel and US: Washington D.C.). To cover both locally (Belgian) and internationally used energy-assessment procedures both the Flemish EPB-software as well as the PHPP-software are used. Furthermore, dynamic simulations in Trnsys are carried out to obtain more detailed and accurate feedback on the buildings’ dynamic thermal response. Through simulations with these three calculation methods, energy robustness is tested and alternative solutions for the building envelope are proposed, adapting the building to its boundary conditions. This paper presents the results from this study, analyzing the differences due to the climate, the calculation method and the design options.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".